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Implementation Of Machine Learning For Web-Based Stroke Probability Prediction Zuhaira Agustari; Roberto Kaban; Safarul Ilham
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 1 (2025): JCEIT: Journal of Computer Engineering and Information Technology (Nov 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i1.36

In an effort to enhance early detection and prevention of stroke, the implementation of web-based machine learning provides a promising solution. This study focuses on applying machine learning algorithms to predict the likelihood of stroke occurrence based on patient medical data collected online. By using the developed prediction model, the system efficiently analyzes historical data and health risk factors to provide stroke risk estimates. This implementation aims to improve diagnostic accuracy, enable better early detection, and offer appropriate preventive recommendations. The results of this study are expected to assist healthcare professionals and patients in stroke prevention efforts through the utilization of web-based technology. REFERENCES Akmaluddin, M., & Dewayanto, T. (2023). Systematic Literature Review: Implementasi Artificial Intelligence dan Machine Learning pada bidang akuntansi manajemen. Diponegoro Journal of Accounting, 12(4), 1–11. http://ejournal-s1.undip.ac.id/index.php/accounting Byna, A., & Basit, M. (2020). Penerapan Metode Adaboost untuk Mengoptimasi Prediksi Penyakit Stroke dengan Algoritma Naïve Bayes. 09(November), 407–411. Cahyono, D. S., Nugrahanti, F., & Hendrawan, A. T. (2019). Aplikasi pemasaran berbasis website pada percetakan Morodadi Komputer Magetan. Prosiding Seminar Nasional Teknologi Informasi dan Komunikasi (SENATIK), 2(1), 129–134. Fahrizal, Reynaldi, F. O., & Hikmah, N. (2020). Implementasi machine learning pada sistem pets identification menggunakan Python berbasis Ubuntu. JISICOM (Journal of Information System, Informatics and Computing), 4(1), 86–91. Hasibuan, E., Informasi, S., Ilmu, F., Informasi, T., Gunadarma, U., Margonda, J., No, R., Cina, P., & Jawa, D. (2022). Implementasi machine learning untuk prediksi harga mobil bekas dengan algoritma regresi linear berbasis web. Jurnal Ilmiah Komputasi, 21(4), 595–602. https://doi.org/10.32409/jikstik.21.4.3327 Igfirly Mustaib, R., Dwiyansaputra, R., Muaidi, M., Desa Sandik Jl Pariwisata, K., & Layar, B. (n.d.). Sistem informasi company profile Kantor Desa Sandik berbasis website (Website based information system of company profile for Sandik Village). Kusuma, A. S., & Nita, S. (2019). Rancang bangun media pembelajaran pengenalan tumbuhan bagi penyandang tuna rungu pada SDLB Manisrejo Kota Madiun. Seminar Nasional Teknologi Informasi dan Komunikasi 2019, 281–286. Metode, M., Di, R. A. D., & Ahmad, S. (2022). No Title, 11(1), 79–85. Prediksi, A., Stroke, D., & Pendekatan, D. (2022). Analisis prediksi deteksi stroke dengan pendekatan EDA dan perbandingan algoritma machine learning. 02, 355–367. Purwono, P., Dewi, P., Wibisono, S. K., Dewa, B. P., Informatika, P., Bangsa, U. H., Keperawatan, P., & Bangsa, U. H. (2022). Model prediksi otomatis jenis penyakit hipertensi dengan pemanfaatan algoritma machine learning Artificial Neural Network. 7(2), 82–90. Putra, A. I., & Santika, R. R. (2020). Implementasi machine learning dalam penentuan rekomendasi musik dengan metode Content-Based Filtering. Edumatic: Jurnal Pendidikan Informatika, 4(1), 121–130. https://doi.org/10.29408/edumatic.v4i1.2162 Stacyana Jesika, S., Ramadhani, S., & Putri, Y. P. (2023). Implementasi model machine learning dalam mengklasifikasi kualitas air. Jurnal Ilmiah dan Karya Mahasiswa, 1(6), 382–396. https://doi.org/10.54066/jikma.v1i6.1162 Ula, M., Ulva, A. F., & Mauliza, M. (2021). Implementasi machine learning dengan model Case Based Reasoning dalam mendiagnosa gizi buruk pada anak. Jurnal Informatika Kaputama (JIK), 5(2), 333–339. https://doi.org/10.59697/jik.v5i2.267 Utama, T. P., & Haibuan, M. S. (2023). Penerapan algoritma Naïve Bayes dan Forward Selection untuk prediksi penyakit stroke. 17, 351–357.

The Effect of Social Media Use on Student Learning Motivation at Era Utama Pancur Batu High School Ginting, Modesta; JM Sembiring, David; Kaban, Roberto; Rangkuti, Nurhafiz Ahmad
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 1 (2025): JCEIT: Journal of Computer Engineering and Information Technology (Nov 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i1.37

Social Media comes with a positive impact and negative impact, epsecially among students and this impact will arise when used excessively. The misuse of social media also often appears in print media in which there is a picture of ironic events and is very different from the main purpose of social media. The worst impact of Facebook’s influence is the declining student learning outcomes. Social Media is a social network that is now increasingly popular and the number of member has increased sharply in a short time. The main task of students is to study and learn, because adolescence is a transitional period that wants to be observed. REFERENCESAgustiah, D., Fauzi, T., & Ramadhani, E. (2020). Dampak penggunaan media sosial terhadap perilaku belajar siswa. Islamic Counseling: Jurnal Bimbingan dan Konseling Islam, 4(2), 181–190. https://doi.org/10.29240/jbk.v4i2.1555Anggarefni, D. (2012). Dampak kegiatan mengakses Facebook terhadap prestasi belajar siswa kompetensi keahlian jasa boga kelas XI di SMK N 3 Wonosari (Skripsi, Universitas Negeri Yogyakarta). Fakultas Teknik, Jurusan Pendidikan Teknik Boga. https://repository.uny.ac.id/Anjaskara, I. (2016). Pengaruh sikap media sosial Instagram terhadap minat beli produk kecantikan melalui Instagram (Skripsi, Universitas Muhammadiyah Yogyakarta). Fakultas Ilmu Sosial dan Ilmu Politik, Jurusan Ilmu Komunikasi. https://repository.umy.ac.id/Feranita. (2017). Pengaruh media sosial Facebook terhadap hasil belajar Akidah Akhlak di MA Syamsul Ulum Kota Sukabumi Jawa Barat (Skripsi, IAIN Raden Intan Lampung). Fakultas Tarbiyah dan Keguruan. https://repository.radenintan.ac.id/Gifary, S., & Kurnia, I. N. (2015). Intensitas penggunaan smartphone terhadap perilaku komunikasi. Jurnal Sosioteknologi, 12(2), 170–178. https://doi.org/10.5614/sostek.itbj.2015.12.2.7Helmi, & Agustina, N. A. (2017). Pengaruh penggunaan gadget terhadap hasil belajar siswa di Sekolah Dasar Negeri 1 Loktabat Utara Kecamatan Banjarbaru. Jurnal Pahlawan, 10(1), 1–12. https://ojs.uniska-bjm.ac.id/index.php/pahlawan/article/view/364Hudaya, A. (2018). Pengaruh gadget terhadap sikap disiplin dan minat belajar peserta didik. Journal of Education, 4(2), 86–97. https://doi.org/10.31227/osf.io/3phz7Istiarini, R. (2012). Pengaruh sertifikasi guru dan motivasi kerja guru terhadap kinerja guru SMA Negeri 1 Sentolo Kabupaten Kulon Progo tahun 2012. Jurnal Pendidikan Akuntansi Indonesia, 10(1), 98–113. https://doi.org/10.21831/jpai.v10i1.923Manumpil, B., Ismanto, Y., & Onibala, F. (2015). Hubungan penggunaan gadget dengan tingkat prestasi siswa di SMA Negeri 9 Manado. E-Journal Keperawatan (e-Kep), 3(2), 1–6. https://ejournal.unsrat.ac.id/index.php/jkp/article/view/9126Mariskhana, K. (2018). Dampak media sosial (Facebook) dan gadget terhadap motivasi belajar. Jurnal, 16(1), 62–67. https://ejurnal.lppmunsera.org/index.php/Jurnal/article/view/127Oktiani, I. (2017). Kreativitas guru dalam memotivasi peserta didik. Jurnal Kependidikan, 5(2), 216–232. https://doi.org/10.24090/jk.v5i2.1934Rahmandani, F., Tinus, A., & Ibrahim, M. M. (2018). Analisis dampak penggunaan gadget (smartphone) terhadap kepribadian dan karakter peserta didik di SMA Negeri 0 Malang. Jurnal Civic Hukum, 3(1), 18–44. https://doi.org/10.22219/jch.v3i1.5302Sardiman. (2011). Interaksi dan motivasi belajar mengajar. PT Raja Grafindo Persada.Setiadi, A. (2016). Pemanfaatan media sosial untuk efektivitas komunikasi. Cakrawala: JurnalHumaniora, 16(2), 87–98. https://doi.org/10.31294/jc.v16i2.1816 Sugiyono. (2016). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta

Application of The FR-04 Sensor for Automatic and Energy-Saving Clothesline Roof Control Buulolo, Morina; Laia, Carles Wiranto; Buulolo, Nafaoli; Kaban, Roberto; Siringo-ringo, Rimmar
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 1 (2025): JCEIT: Journal of Computer Engineering and Information Technology (Nov 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i1.38

The application of the FR-04 sensor for automatic and energy-saving clothesline roof control is based on problems that people who have clothesline, so that dry clothes become wet with rainwater when the occupants of the house are outside the house. This system uses an FR-04 sensor to detect rain and an Arduino Uno microcontroller to control the Stepper Motor which opens and closes the clothesline roof. It is hoped that the creation of an automatic clothes drying roof design will help people reduce their anxiety when drying clothes in the rainy season. REFERENCESAlfiansyah, M. N., & Nugroho, A. (2023). Automatic clothesline control system using rain and light sensors for energy efficiency. International Journal of Electrical and Computer Engineering (IJECE), 13(1), 657–665. https://doi.org/10.11591/ijece.v13i1.pp657-665Arfianto, D. (2021). Prototipe jemuran otomatis dengan sensor hujan, LDR berbasiskan Arduino Uno R3 dan sistem monitoring menggunakan aplikasi Blynk. Seminar Nasional Manajemen, Informatika, dan Komputer(SENAMIKA), 269–277. https://ejurnal.teknokrat.ac.id/index.php/senamika/article/view/1230Fedianto, M. H. S., Aditiawan, F. P., & Al Haromainy, M. M. (2023). Pengujian sistem jaringan dokumentasi dan informasi menggunakan black box testing dan white box testing. Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis, 3(1), 213–221. https://doi.org/10.55606/jupsim.v3i1.2447Fitria, D., & Hidayat, A. (2022). Pengembangan sistem atap jemuran otomatis dengan sensor FR-04 dan konektivitas Blynk berbasis IoT. Jurnal Teknologi Rekayasa dan Inovasi, 4(3), 145–153. https://journal.inovasi.tech/index.php/jtri/article/view/895Hafidhin, M. I., Saputra, A., Rahmanto, Y., & Samsugi, S. (2020). Alat penjemuran ikan asin berbasis mikrokontroler Arduino UNO. Jurnal Teknik dan Sistem Komputer, 1(2), 59–66. https://doi.org/10.33365/jtikom.v1i2.210Jadhav, R. S., & Deshmukh, P. B. (2020). Design and development of an automated clothes drying system using IoT and environmental sensors. International Research Journal of Engineering and Technology (IRJET), 7(5), 3172–3178. https://www.irjet.net/archives/V7/i5/IRJET-V7I5661.pdfKencana, W., A. G. A. T. H. A., & Dan, F. T. I. (2020). Rancang bangun alat otomatis hand sanitizer dan ukur suhu tubuh mandiri untuk pencegahan Covid-19 berbasis IoT. Jurnal Transit, 1–6. https://ojs.trigunadharma.ac.id/index.php/transit/article/view/1226Kumar, A., & Gupta, R. (2020). IoT-enabled smart home energy-saving roof control system. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(6), 120–125. https://doi.org/10.35940/ijitee.F3795.049620Li, X., Wang, S., & Zhao, Y. (2022). Smart IoT-based rain detection and automatic awning control system for household energy saving. Sensors, 22(14), 5318. https://doi.org/10.3390/s22145318Luo, H., Chen, W., & Li, P. (2021). Design of an automatic retractable roof system using IoT-based rain sensors and servo control. Measurement, 180, 109559. https://doi.org/10.1016/j.measurement.2021.109559Mustar, R. O. W. M. Y. (2017). Implementasi sistem monitoring deteksi hujan dan suhu berbasis sensor secara real time. Semesta Teknik, 20(1), 20–28. https://jurnal.umj.ac.id/index.php/semesta/article/view/1865Nguyen, T. P., & Le, H. N. (2021). Energy-efficient automatic roof control for smart homes using IoT sensors and adaptive algorithms. Journal of Building Engineering, 44, 103240. https://doi.org/10.1016/j.jobe.2021.103240Nurhasanah, L., & Ramadhan, M. (2023). Penerapan sensor FR-04 dan LDR pada sistem atap otomatis hemat energi menggunakan ESP32. Jurnal Riset Teknologi Elektro dan Komputer, 8(1), 33–42. https://ejurnal.teknokrat.ac.id/index.php/jrtek/article/view/2180Prasetyo, Y., & Zainuddin, A. (2020). Smart clothesline roof system based on FR-04 rain sensor and solar power controller. Jurnal Teknologi Informasi dan Elektronika, 5(4), 201–210. https://ejurnal.poliban.ac.id/index.php/jtie/article/view/1117Putra, A. P., & Wicaksono, D. (2022). Perancangan sistem jemuran otomatis berbasis Internet of Things menggunakan sensor FR-04 dan NodeMCU ESP8266. Jurnal Teknologi dan Sistem Terkini, 3(2), 101–110. https://doi.org/10.33365/jtst.v3i2.1142Rahman, M., & Anwar, S. (2023). IoT-based automatic roof control system using rainfall and humidity sensors for household energy saving. IEEE Access, 11, 45327–45338. https://doi.org/10.1109/ACCESS.2023.3267554Setiawan, F., & Haryanto, R. (2021). Sistem pengendali atap otomatis berbasis sensor hujan FR-04 dan sensor cahaya LDR dengan Arduino UNO. Jurnal Elektro dan Instrumentasi, 7(2), 88–95. https://jurnal.untidar.ac.id/index.php/jei/article/view/2230Sutanto, D., & Priyanto, A. (2022). Analisis kinerja sensor FR-04 dalam mendeteksi intensitas hujan pada sistem otomatisasi berbasis mikrokontroler. Jurnal Teknik Elektro Indonesia, 12(1), 55–63. https://doi.org/10.22146/jtei.v12i1.9803Taufik, M., & Rachman, A. (2021). Rancang bangun sistem atap jemuran otomatis menggunakan sensor hujan FR-04 berbasis Arduino Uno. Jurnal Teknik Elektro dan Komputer, 10(3), 112–119. https://doi.org/10.33369/jtekkom.v10i3.1789Zhang, Y., & Liu, C. (2024). IoT-based automatic rain detection system for smart domestic control applications. IEEE Internet of Things Journal, 11(2), 14012–14020. https://doi.org/10.1109/JIOT.2024.3360124

Heartbeat Detector Using ESP32 Microcontroller and Website-Based Pulse Sensor Hulu, Helisati; Hulu, April Imelda Putri; Giawa, Fazzasokhi; Harefa, Sukur; Peranginanagin, Sinek Mehuli Br; Ilham, Safarul
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 1 (2025): JCEIT: Journal of Computer Engineering and Information Technology (Nov 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i1.39

In todays digital era, the demand for adviced and user-friendly healthcare device is increasing. This research aims to design and develop a real- time heart rate detection device using ESP32 microcontroller and a pulse sensor connected to a web platform. This device is designed to continuously monitor the user’s heart rate and provide online data access, facilitating easier health monitoring. The methology used in this research includes the design of hardware and software. The ESP32 microcontroller was chosen for its advantages in Wi-Fi connetivity and good data processing capabilities. A pulse sensor is used to detect the heart rate, while a web platform is developed to display real time heart rate data. The research results show that this custom-bulit heart rate detection device can effectively dectect and display real-time heart rate data. The data sent to the web platform can be accessed via computer or smartphone making it easier for users or medical personnel to monitor health conditions remotely. In summary, this heart rate detector based on the ESP32 microcontroller and pulse sensor successfully meets the need for real-time heart rate monitoring and can be accessed via the web. Futher development can be done to improve accuracy and integration with other medical systems.REFERENCESAdiyanti, R., et al. (2021). Perancangan sistem informasi indeks penyakit rawat inap menggunakan Microsoft Visual Studio. Jurnal Teknologi dan Manajemen Informatika, 7.Ambary, I. M., & Raharja, W. K. (2018). Purwarupa alat pendeteksi detak jantung berbasis Atmega328. Jurnal Ilmiah Teknologi dan Rekayasa, 23(1), 38–47. https://doi.org/10.35760/tr.2018.v23i1.2449Ananda, Y. (2021). Perancangan sistem informasi detak jantung berbasis Android phone menggunakan media Bluetooth. Jurnal Simetri Rekayasa, 5(3), 170–174. https://jurnal.harapan.ac.id/index.php/JSRAprilia, et al. (2020). Sistem monitoring real time detak jantung dan kadar oksigen dalam darah pada manusia berbasis Internet of Things. Jurnal Ilmiah Foristek, 10, 96–102.Azis, N., et al. (2020). Analisa dan perancangan aplikasi pembelajaran bahasa Inggris dasar berbasis Android. IKRA-IKT Informatika, 4(2), 45–52.Ikhsani, R. B., et al. (2022). Monitoring pengukur detak jantung dan suhu tubuh pada pasien berbasis Internet of Things. Jurnal Informatika dan Teknik Elektro Terapan, 10(1), 22–29.Kamal, et al. (2023). Implementasi aplikasi Arduino IDE pada mata kuliah sistem digital. Jurnal Pendidikan dan Teknologi, 1(1), 45–52.Ladjamuddin, et al. (2021). Rancang bangun aplikasi pembelajaran simulasi tata surya menggunakan augmented reality berbasis Android. INCOMTECH, 10(2), 85–92.Manurung, S., et al. (2021). Penggunaan sistem Arduino menggunakan RFID untuk keamanan kendaraan bermotor. Jurnal Penelitian Inovatif, 1(2), 138–145. https://doi.org/10.54082/jupinMuliadi, et al. (2020). Pengembangan tempat sampah pintar menggunakan ESP32. Jurnal Media Elektronika, 2(1), 22–29.Nizam, M., et al. (2022). Mikrokontroler ESP32 sebagai alat monitoring pintu berbasis web. Jurnal Mahasiswa Teknik Informatika, 6(1), 30–37.Perangin-angin, A., et al. (2021). Rancang bangun alat memprediksi Covid-19 pada perkantoran menggunakan GY-90614 dan pulse sensor berbasis Arduino. Jurnal Cybertech, 4(1), 15–23.Pramukantoro, E. S., et al. (2024). Implementasi sensor Polar H10 dan Raspberry Pi dalam pemantauan dan klasifikasi detak jantung beberapa individu secara simultan dengan pendekatan machine learning. Jurnal Teknologi Informasi dan Ilmu Komputer, 11(2), 175–186. https://doi.org/10.25126/jtiik.20241117716Pratama Hudhajanto, R., Mulyadi, I. H., & Sandi, A. A. (2022). Wearable sensor device berbasis IoT berbentuk face shield untuk memonitor detak jantung. Journal of Applied Informatics and Computing. http://jurnal.polibatam.ac.id/index.php/JAICRahman, M. F., et al. (2020). Deteksi sampah pada real-time video menggunakan metode Faster R-CNN. Applied Technology and Computer Science Journal, 3(2), 120–128.Rahmad Timor, A., & Kesuma, D. (2023). Alat pengukur denyut nadi dengan tampilan OLED berbasis Arduino. Jurnal Juni, 2(1), 92–97.Rasmila, et al. (2024). Simulasi sistem monitoring kenaikan level air pada area rawan banjir secara real time berbasis smartphone Android. Jurnal Pengembangan Sistem Informasi dan Informatika, 5(1), 47– 55.Santoso, S. P., et al. (2022). Rancang bangun akses pintu dengan sensor suhu dan hand sanitizer otomatis berbasis Arduino. Jurnal Elektro, 10(1), 30–38.Wulandari, R. (2020). Rancang bangun pengukur suhu tubuh berbasis Arduino sebagai alat deteksi awal Covid-19. In Prosiding Seminar Nasional Fisika dan Aplikasinya (SNFA).

Design and Construction of an Internet of Things (IoT)-Based Smart Garden System Badriana, Badriana; Ramadhani, Putri; Hidayat, Muhammad; Sinuhaji, Sebastian Ferdi Caras; Nasution, Darmeli; Surbakti, Aprina Br
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 1 (2025): JCEIT: Journal of Computer Engineering and Information Technology (Nov 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i1.40

The use of the Internet of Things is currently very useful for the plantation sector, especially mustard greens. The Smart Garden System is still not widely found on plantation land. Smart Garden comes from English which means garden or smart garden, which is a system design created to make work in plantations easier. The aim of making this tool is to help in managing the plants, apart from helping in managing the plants, this Smart Garden can also be used to measure the humidity/air content in the soil, because some people who are cultivating do not know/cannot distinguish which soil is good for certain crops are planted and which are not, resulting in many crops failing to harvest and being detrimental because they wither and die. So it is hoped that this smart garden system can help many farmers in maintaining soil moisture conditions and can minimize damage to plants. "Smart Garden System" Refers to the concept of technology integration in garden or agricultural management to increase efficiency, productivity and loss. This Smart Garden System uses an ESP32 Microcontroller as data processing and control for Soil hygrometer, LED, 16x2 LCD, Water Washer and other components. REFERENCESAbdullah, R. (2021). 7 in 1 pemrograman web untuk pemula. PT Elex Media Komputindo.Abdur Rochman, M. I., Hanafri, M. I., & Wandira, A. (2020). Implementasi website profil SMK Kartini sebagai media promosi dan informasi berbasis open source. Academic Journal of Computer ScienceResearch (AJCSR), 2(1), 1–6. https://ajcsr.ejournal.id/ajcsr/article/view/33Adani, M. R. (2020). Mengenal apa itu internet of things dan contoh penerapannya.https://www.sekawanmedia.co.idAli, M. M., Sukaca, G., & Aji, Z. P. (2020). Pengaruh ketahanan insulasi pada lampu LED swabalastterhadap keselamatan pengguna sesuai SNI IEC 62560. Jurnal Teknik Elektro dan Komputer, 9(2),80–86.Anwar, A., et al. (2020). Sistem pendeteksi kandungan nutrisi dalam tanah berdasarkan warna dankelembapan menggunakan metode Naive Bayes. Jurnal Pengembangan Teknologi Informasi dan IlmuKomputer, 4(8), 2763–2770. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/8021Chabir, S. O., & Kunang. (2020). Prototype smart garden system berbasis mikrokontroler. Bina DarmaConference on Engineering Science, 2, 10–19.Darmawan, I. W. B., Kumara, I. N. S., & Khrisne, D. C. (2022). Smart garden sebagai implementasisistem kontrol dan monitoring tanaman berbasis teknologi cerdas. Jurnal SPEKTRUM, 8(4), 161–168.https://doi.org/10.24843/SPEKTRUM.2022.v08.i04.p05De, A., & Singh, R. (2020). Organic production of Cymbidium orchids. Acta Scientific Agriculture, 4(3),17–21. https://doi.org/10.31080/ASAG.2020.04.0812Efendi, Y. (2018). Internet of things (IoT): Sistem pengendalian lampu menggunakan Raspberry Piberbasis mobile. Jurnal Ilmiah Ilmu Komputer, 4(2), 7–12.https://ejournal.unira.ac.id/index.php/jikom/article/view/191Gulo, S., & Simamora, R. J. (2020). Perancangan sistem informasi administrasi rawat inap dan rawatjalan pada Rumah Sakit Umum Siti Hajar. Methomika: Jurnal Manajemen Informatika &Komputerisasi Akuntansi, 2(1), 30–42. https://doi.org/10.33365/methomika.v2i1.527Gunawan, I., Ahmadi, H., & Said, M. R. (2021). Rancang bangun sistem monitoring dan pemberi pakan otomatis ayam anakan berbasis internet of things. Infotek: Jurnal Informatika dan Teknologi, 4(2),151–162. https://doi.org/10.29408/jit.v4i2.4707Hu, A. Q., & Yahya. (2020). Rancang bangun aplikasi perpustakaan menggunakan frameworkCodeIgniter. Jurnal Sistem Informasi dan Sains Teknologi, 2(2), 45–51.Jordy, A. B. (2021). Rancang bangun dan analisis kinerja perangkat transceiver sistem visible lightcommunication (Skripsi sarjana). Universitas Telkom.Lestari, K. C., & Amri, A. M. (2020). Sistem informasi akuntansi. Deepublish.Maydianto, & Ridho, M. R. (2021). Rancang bangun sistem informasi point of sale dengan frameworkCodeIgniter. Jurnal Comasie, 2, 50–59.Mulyanto. (2020). JINTEKS: Jurnal Inovasi Teknik dan Sistem Komputer, 2(1).Nistrina, K., & Rahmania, A. (2021). Sistem informasi point of sale berbasis website. Jurnal SistemInformasi, 3(2), 56–63.Rajkumar, B., et al. (2020). Internet of things: Principles and paradigms. Elsevier.https://doi.org/10.1016/C2018-0-01735-5Retno, D., Zain, H., Ipriadi, E. P., & Rahmawati, S. (2021). Teknologi internet of things dalampenyemprotan insektisida aglonema pada greenhouse. Jurnal Teknologi, 11(2), 36–43.https://doi.org/10.31849/jtek.v11i2.6782Romli, I., Hugo, K. L. N., & Afriantoro, I. (2021). Perancangan dan implementasi smart garden berbasisinternet of things pada perumahan Central Park Cikarang. Indonesian Journal of Business Intelligence,4(2), 42–52. https://doi.org/10.21927/ijubi.2021.4(2).42-52Sasmito, G. W., & Wijayanto, S. (2020). 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Early Detection System for Heartbeat Abnormalities in Autistic Children Using Support Vector Machine Ridwan, Ahmad
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 1 (2025): JCEIT: Journal of Computer Engineering and Information Technology (Nov 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i1.45

This research aims to develop an early detection system for heart rate anomalies in autistic children based on Heart Rate Variability (HRV) to prevent tantrum behavior that can endanger the child's physical and psychological health. Based on previous research, children with autism spectrum disorder (ASD) show a significant increase in heart rate (HR), especially when experiencing stress or anxiety, with some cases reaching above 120 bpm. At the same time, control groups such as children with language disorders do not show a similar pattern. This leads to the hypothesis that physiological monitoring using non-invasive technologies, such as Photoplethysmography (PPG), can detect changes in HR before a tantrum occurs. The purpose of this study is to design a wearable device based on a pulse sensor and NodeMCU that can integrate HR in real-time, extract HRV features in the frequency domain (VLF, LF, HF, and LF/HF ratio), and classify normal and anomalous conditions using the Support Vector Machine (SVM) algorithm. The system is designed to notify parents or caregivers via a Telegram bot when HR exceeds 114 bpm. The research methodology was experimental, conducted on two subjects: a 7-year-old boy and a girl on the autism spectrum during learning, quiet, and tantrum activities. Results showed that HRV parameters increased significantly during the tantrum condition and even during learning, indicating activation of the sympathetic nervous system. The SVM classifier achieved 98.9% accuracy in the tantrum condition, 82% in the learning condition, but only 61.1% in the transition from quiet to tantrum. Overall, the system proved effective at detecting hyperactivity but still requires further development regarding data volume, subject variation, and improvements in accuracy during the transition phase for widespread implementation. REFERENCES Aldabas, R. (2019). Effectiveness of social stories for children with autism: A comprehensive review. Technology and Disability, 31(1–2), 1–13. https://doi.org/10.3233/TAD-180218 Awanda Amelia Sadita, & Nurus Sa’adah. (2023). Temper Tantrum Behavior in Early Childhood as Communication with Parents. Journal of Insan Mulia Education, 1(2), 45–52. https://doi.org/10.59923/joinme.v1i2.7 Beauchamp-Châtel, A., Courchesne, V., Forgeot d’Arc, B., & Mottron, L. (2019). Are tantrums in autism distinct from those of other childhood conditions? A comparative prevalence and naturalistic study. Research in Autism Spectrum Disorders, 62(March), 66–74. https://doi.org/10.1016/j.rasd.2019.03.003 Chen, C., Li, C., Tsai, C. W., & Deng, X. (2019). Evaluation of Mental Stress and Heart Rate Variability Derived from Wrist-Based Photoplethysmography. Proceedings of 2019 IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2019, 65–68. https://doi.org/10.1109/ECBIOS.2019.8807835 Deichmann, F., & Ahnert, L. (2021). The terrible twos: How children cope with frustration and tantrums and the effect of maternal and paternal behaviors. Infancy, 26(3), 469–493. https://doi.org/10.1111/infa.12389 Farahdina, Irwanto, & Fithriyah, I. (2025). Risk factors for autism spectrum disorder diagnosed in Indonesia. Child`S Health, 20(5), 325–332. https://doi.org/10.22141/2224-0551.20.5.2025.1866 Fioriello, F., Maugeri, A., D’Alvia, L., Pittella, E., Piuzzi, E., Rizzuto, E., Del Prete, Z., Manti, F., & Sogos, C. (2020). A wearable heart rate measurement device for children with autism spectrum disorder. Scientific Reports, 10(1), 1–7. https://doi.org/10.1038/s41598-020-75768-1 Islmabouli, R., Brunner, M., Kumar, D., Sareban, M., & ... (2025). Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices. ArXiv Preprint ArXiv https://arxiv.org/abs/2508.19818%0Ahttps://arxiv.org/pdf/2508.19818 McCorry, L. K. (2007). Physiology of the Autonomic Nervous System. American Journal of Pharmaceutical Education, 71(4), 1–11. https://doi.org/10.1111/j.1399-6576.1964.tb00252.x Novani, N. P., Arief, L., & Anjasmara, R. (2019). Analisa Detak Jantung dengan Metode Heart Rate Variability (HRV) untuk Pengenalan Stres Mental Berbasis Photoplethysmograph (PPG). JITCE (Journal of Information Technology and Computer Engineering), 3(02), 90–95. https://doi.org/10.25077/jitce.3.02.90-95.2019 Novani, N. P., Arief, L., Anjasmara, R., & Prihatmanto, A. S. (2018). Heart Rate Variability Frequency Domain for Detection of Mental Stress Using Support Vector Machine. 2018 International Conference on Information Technology Systems and Innovation, ICITSI 2018 - Proceedings, 520–525. https://doi.org/10.1109/ICITSI.2018.8695938 Pinge, A., Bandyopadhyay, S., Ghosh, S., & Sen, S. (2022). A Comparative Study between ECG-based and PPG-based Heart Rate Monitors for Stress Detection. 2022 14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022, 84–89. https://doi.org/10.1109/COMSNETS53615.2022.9668342 Thapa, R., Pokorski, I., Ambarchi, Z., Thomas, E., Demayo, M., Boulton, K., Matthews, S., Patel, S., Sedeli, I., Hickie, I. B., & Guastella, A. J. (2021). Heart Rate Variability in Children With Autism Spectrum Disorder and Associations With Medication and Symptom Severity. Autism Research, 14(1), 75–85. https://doi.org/10.1002/aur.2437 Tsai, Y. Y., Chen, Y. J., Lin, Y. F., Hsiao, F. C., Hsu, C. H., & Liao, L. De. (2025). Photoplethysmography-based HRV analysis and machine learning for real-time stress quantification in mental health applications. APL Bioengineering, 9(2). https://doi.org/10.1063/5.0256590 Weiler, D. T., Villajuan, S. O., Edkins, L., Cleary, S., & Saleem, J. J. (2017). Wearable Heart Rate Monitor Technology Accuracy in Research: A Comparative Study Between PPG and ECG Technology. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), 1292–1296. https://doi.org/10.1177/1541931213601804 Zhang, Y., Song, S., Vullings, R., Biswas, D., Simões-Capela, N., Van Helleputte, N., Van Hoof, C., & Groenendaal, W. (2019). Motion artifact reduction for wrist-worn photoplethysmograph sensors based on different wavelengths. Sensors (Switzerland), 19(3). https://doi.org/10.3390/s19030673

Product Segmentation for Apparel MSMEs Using K-Means and CRISP-DM Approach Hidayat, Rahmat
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 2 (2026): JCEIT: Journal of Computer Engineering and Information Technology (March 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i2.44

Apparel Micro, Small, and Medium Enterprises (MSMEs) frequently provide significant sales data that is inadequately leveraged for strategic business advancement. This study intends to examine sales data utilising the K-Means Clustering technique within the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The dataset consists of 360 sales records during the period 2021–2023, including factors such as amount sold, unit price, and total turnover. The analysis adheres to the systematic CRISP-DM phases: business understanding, data understanding, data preparation, modelling, assessment, and deployment. The assessment outcomes, quantified by the Silhouette Score, determined two ideal clusters for the dataset. Cluster 1 denotes well-performing products distinguished by elevated sales volume, comparatively high unit prices, and substantial turnover. Conversely, Cluster 2 comprises underperforming products characterised by diminished sales volume, reduced unit pricing, and negligible turnover. These findings offer a data-driven basis for MSMEs to develop more efficient marketing strategies and inventory management procedures. By categorising products according to performance, business owners can prioritise high-value commodities and optimise inventory for less productive categories. This study indicates that employing K-Means clustering inside the CRISP-DM framework effectively converts raw sales data into meaningful business intelligence for the garment sector. Subsequent study may enhance this technique by integrating external variables, including seasonal trends or customer demographics, to improve clustering precision. REFERENCES Abdul-Azeez, O., Ihechere, A. O., & Idemudia, C. (2024). Enhancing business performance: The role of data-driven analytics in strategic decision-making. 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Peningkatan akurasi deteksi liver disease melalui hyperparameter tuning pada algoritma random forest. Indonesian Journal of Computer Science Research, 4(2), 139–147. Barrera, F., Segura, M., & Maroto, C. (2023). Multicriteria sorting method based on global and local search for supplier segmentation. International Transactions in Operational Research, 31, 3108–3134. https://doi.org/10.1111/itor.13288 Daniati, E., & Utama, H. (2019). Clustering K-Means for criteria weighting with improvement result of alternative decisions using SAW and TOPSIS. In 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) (pp. 73–78). IEEE. https://doi.org/10.1109/ICITISEE48480.2019.9003858 Dewata, E., Sari, Y., & Jauhari, H. (2020). Penyusunan laporan keuangan terkomputerisasi berdasarkan SAK EMKM pada UMKM konveksi. Intervensi Komunitas, 2(1), 11–16. https://doi.org/10.32546/ik.v2i1.676 Fahrudin, N. F., & Rindiyani, R. (2024). 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Normalisasi data untuk efisiensi K-Means pada pengelompokan wilayah berpotensi kebakaran hutan dan lahan berdasarkan sebaran titik panas. Teknik Teknologi Informasi dan Multimedia, 2(2), 83–89. https://doi.org/10.46764/teknimedia.v2i2.49 He, Y., Xu, Z., & Liu, N. (2022). Research on K-medoids algorithm with probabilistic-based expressions and its applications. Applied Intelligence, 52, 12016–12033. https://doi.org/10.1007/s10489-021-02937-8 Henderi, H., Fitriana, L., Iskandar, I., Astuti, R., Arifandy, M. I., Hayadi, B., Mesran, M., Chin, J., & Kurniawan, A. (2024). Optimization of Davies-Bouldin index with K-medoids algorithm. AIP Conference Proceedings. https://doi.org/10.1063/5.0225220 Hidayati, R., Zubair, A., Pratama, A. H., & Indana, L. (2021). Analisis silhouette coefficient pada 6 perhitungan jarak K-Means clustering. Techno.Com, 20(2), 186–197. https://doi.org/10.33633/tc.v20i2.4556 Huang, Y., Zhang, M., & He, Y. (2020). 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Analisis segmentasi recency dan customer value pada AVANA Indonesia dengan algoritma K-means dan model RFM. Journal of Information System Research (JOSH). https://doi.org/10.47065/josh.v4i2.2950 Kurnia, O. D., et al. (2024). Analisis perbandingan algoritma Naïve Bayes dengan K-Nearest Neighbor (KNN) pada dataset mobile price classification. Prosiding Seminar Nasional Inovasi Teknologi (SEMNAS INOTEK), 8, 2549–7952. Ma, M. (2024). The application of K-means algorithm-based data mining in optimizing marketing strategies of tobacco companies. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/ijacsa.2024.0151186 Maniyara, K., Shah, K., Gaidhane, V. H., & Wanjari, R. (2024). An effective customer segmentation using RFM ranking techniques. In 2024 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) (pp. 592–597). https://doi.org/10.1109/MoSICom63082.2024.10882067 Maulana, A., Ristyawan, A., Ndun, A. R., & Ristyawan, A. (2025). Prediksi volume sampah perkotaan berbasis data spasial menggunakan random forest di DKI Jakarta. Prosiding SEMNAS INOTEK, 9, 1667–1672. Hidayat, R., & Aminulhaq, F. (2025). A Comparative Analysis of Decision Tree, Logistic Regression, and Support Vector Machine Algorithms in Sentiment Analysis of Threads App Reviews. Intechno Journal: Information Technology Journal, 7(2), 45-55. Hidayat, R., & Ratnaningsih, D. J. (2025). Analisis Sentimen Program Mbg Menggunakan Algoritma Random Forest Dan Naive Bayes. Journal of Computing and Informatics Research, 5(1), 395-400. Hidayat, R. (2026). The Effect of Ability and Career Development on Employee Performance: Evidence from the Indonesian Automotive Industry. Jurnal Prima Manajemen, 1(3), 535-547. Mirantika, N., & Rijanto, E. (2024). Implementasi metode clustering partisi dalam menentukan segmentasi pelanggan. Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi. https://doi.org/10.34010/jtk3ti.v10i1.11320 Mirantika, N., & Rijanto, E. (n.d.). Comparative analysis of K-means and K-medoids algorithms in determining customer segmentation using RFM model. Retrieved from https://www.semanticscholar.org/paper/2053b7b55a15bd710794443298214c8305d29458 Nasution, M. Z., & Hasibuan, M. S. (2020). Pendekatan initial centroid search untuk meningkatkan efisiensi iterasi klustering K-Means. Techno.Com, 19(4), 341–352. https://doi.org/10.33633/tc.v19i4.3875 Niu, J. (2021). Intelligent evaluation model of e-commerce transaction volume based on the combination of K-means and SOM algorithms. International Journal of Information and Communication Technology, 18, 189–206. https://doi.org/10.1504/ijict.2021.10034321 Nugroho, B. I., Rafhina, A., Ananda, P. S., & Gunawan, G. (2024). Customer segmentation in sales transaction data using K-means clustering algorithm. 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Mapping Research Trends of Query Expansion in Information Retrieval: A Bibliometric Analysis Kaban, Roberto
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 2 (2026): JCEIT: Journal of Computer Engineering and Information Technology (March 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i2.57

This study aims to analyze the development of research on query expansion in the field of information retrieval using a bibliometric approach to understand research trends, distribution, and current research focus. The data were obtained from 676 publications indexed in Scopus during the period from 2020 to February 2026. The research method involves quantitative analysis of annual publication trends, distribution of subject areas, document types, and keyword analysis using VOSviewer to map keyword relationships through co-occurrence analysis, overlay visualization to identify keyword trends, and density visualization to observe the concentration of research topics. The results show fluctuations in the number of publications with a peak occurring in 2025 with 141 publications. The research is dominated by the Computer Science field with 596 publications, and the majority of documents are conference papers with 369 publications. Keyword analysis identifies core topics such as information retrieval with 483 occurrences, query expansion with 354 occurrences, and search engines with 221 occurrences. Recent research trends include large language models, word embedding, and retrieval-augmented generation. The keyword network visualization indicates a shift from traditional methods such as relevance feedback toward modern approaches based on artificial intelligence and machine learning, which are increasingly relevant for improving the effectiveness of information retrieval systems. These findings provide both quantitative and qualitative insights into the evolution of query expansion research. The results also highlight the integration of modern technologies in retrieval practices and provide a foundation for new researchers to identify trends, research gaps, and opportunities for future innovation. REFERENCES Ahmed, M. (2024). 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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Roberto Kaban

Contact Email
itgeek.id@gmail.com

Phone
+6281260329842

Journal Mail Official
jceit@jurnal.ktsi.my.id

Editorial Address
Karya Techno Solusindo Berkala ASRI Blok R No. 10, Jalan Kapiten Purba IIDesa /Kelurahan Mangga, Kecamatan Medan Tuntungan, Medan, Sumatera Utara 20141

Location
Kota medan,

Sumatera utara

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Roberto Kaban

Contact Email itgeek.id@gmail.com

Phone +6281260329842

Journal Mail Official jceit@jurnal.ktsi.my.id

Editorial Address Karya Techno Solusindo Berkala ASRI Blok R No. 10, Jalan Kapiten Purba IIDesa /Kelurahan Mangga, Kecamatan Medan Tuntungan, Medan, Sumatera Utara 20141

Location Kota medan, Sumatera utara INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Roberto Kaban

Contact Email
itgeek.id@gmail.com

Phone
+6281260329842

Journal Mail Official
jceit@jurnal.ktsi.my.id

Editorial Address
Karya Techno Solusindo Berkala ASRI Blok R No. 10, Jalan Kapiten Purba IIDesa /Kelurahan Mangga, Kecamatan Medan Tuntungan, Medan, Sumatera Utara 20141

Location
Kota medan,

Sumatera utara

INDONESIA