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All Journal Jurnal TIMES Scientific Journal of Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika Jurnal Dinamika Informatika (JDI) Jurnal Informatika dan Perancangan Sistem (JIPS) LOFIAN: Jurnal Teknologi Informasi dan Komunikasi LOGIC : Jurnal Ilmu Komputer dan Pendidikan Jurnal Ilmiah Research Student Blantika : Multidisciplinary Journal Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Journal Of Computer Engineering And Information Technology System Information and Computer Technology (SYNCTECH) Jurnal Ilmu Komputer dan Teknik Informatika

Roberto Kaban

Institut Teknologi dan Bisnis Indonesia

Author-ID : 3762846

Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Other

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Prediksi Diagnosa Penyakit Diabetes Menggunakan Algoritma Decision Tree Berdasarkan Indikator Kesehatan Tubuh Egi Mutiara Br Sitepu; Roberto Kaban
LOGIC : Jurnal Ilmu Komputer dan Pendidikan Vol. 4 No. 2 (2026): Logic : Jurnal Ilmu Komputer dan Pendidikan
Publisher : Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Meningkatnya jumlah penderita diabetes mellitus secara global telah menimbulkan urgensi yang nyata terhadap ketersediaan sistem deteksi dini yang akurat, terjangkau, dan dapat diinterpretasikan secara klinis oleh tenaga medis. Penelitian ini bertujuan mengeksplorasi penerapan algoritma Decision Tree varian C4.5 dalam pembangunan model prediksi diagnosis diabetes yang bersumber dari data klinis secara otomatis. Sebagai dataset, digunakan Pima Indians Diabetes yang diperoleh dari UCI Machine Learning Repository, berisikan 768 rekaman medis perempuan berketurunan Indian Pima dengan delapan variabel prediktor kesehatan. Seluruh tahapan penelitian meliputi eksplorasi awal data, penanganan nilai tidak valid menggunakan imputasi median, normalisasi Min-Max, konstruksi pohon keputusan, serta evaluasi kinerja model dengan metode 10-fold cross-validation. Pengujian pada data independen menghasilkan akurasi sebesar 77,92%, recall 75,93%, presisi 66,13%, F1-Score 70,69%, dan nilai AUC-ROC 0,823. Variabel glukosa plasma tercatat memberikan kontribusi tertinggi terhadap kepentingan fitur yakni sebesar 38,14%, yang menegaskan posisinya sebagai penanda klinis utama dalam diagnosis diabetes. Temuan ini mengindikasikan bahwa algoritma Decision Tree C4.5 berpotensi dijadikan instrumen skrining awal diabetes pada fasilitas layanan kesehatan tingkat pertama.

Klasifikasi Kelayakan Penerima Bantuan Menggunakan Metode Naive Bayes Andara Cantika Effendy; Roberto Kaban
Jurnal Ilmu Komputer dan Teknik Informatika Vol. 2 No. 2 (2026): Juli 2026
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/juikti.v2i2.158

Program bantuan merupakan salah satu upaya yang diselenggarakan untuk mendukung masyarakat dalam memenuhi kebutuhan hidup. Dalam proses penentuan penerima bantuan, diperlukan suatu metode yang mampu mengelompokkan data secara objektif berdasarkan informasi yang tersedia. Penelitian ini bertujuan untuk mengklasifikasikan kelayakan penerima bantuan menggunakan algoritma Naive Bayes berdasarkan karakteristik ekonomi setiap individu. Dataset yang digunakan berasal dari Adult Income Dataset yang diperoleh melalui Kaggle, dengan atribut meliputi usia, tingkat pendidikan, jenis pekerjaan, jumlah jam kerja per minggu, dan pendapatan. Tahapan penelitian mencakup seleksi data, preprocessing, transformasi data, serta penerapan algoritma Naive Bayes. Dataset dibagi menjadi data latih sebesar 70% dan data uji sebesar 30% untuk proses pelatihan serta evaluasi model. Berdasarkan hasil pengujian, model Naive Bayes memperoleh tingkat akurasi sebesar 76,78%, precision 78,70%, recall 95,25%, dan F1-score sebesar 86,19%. Temuan tersebut menunjukkan bahwa algoritma Naive Bayes memiliki kemampuan yang cukup baik dalam mengklasifikasikan kelayakan penerima bantuan berdasarkan kondisi ekonomi individu. Model yang dikembangkan mampu memberikan performa klasifikasi yang memadai pada dataset yang digunakan. Dengan demikian, metode Naive Bayes dapat dijadikan sebagai salah satu alternatif metode klasifikasi untuk mendukung proses pengambilan keputusan dalam menentukan kelayakan penerima bantuan.

Implementasi Algoritma Random Forest untuk Prediksi Customer Churn pada Perusahaan Telekomunikasi Muhammad Junaidi; Roberto Kaban
Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Vol. 3 No. 3 (2026): SINTA: JULI
Publisher : Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/sinta.v3i3.320

Customer churn merupakan permasalahan krusial dalam industri telekomunikasi yang berdampak langsung pada penurunan pendapatan perusahaan. Tantangan utama dalam prediksinya adalah ketidakseimbangan kelas, di mana jumlah pelanggan non-churn jauh lebih besar dibandingkan pelanggan churn. Penelitian ini mengimplementasikan algoritma Random Forest dikombinasikan dengan Synthetic Minority Oversampling Technique (SMOTE) untuk mengatasi permasalahan tersebut. Dataset yang digunakan adalah Telco Customer Churn dari Kaggle, terdiri dari 7.043 data pelanggan dengan 21 atribut. Pra-pemrosesan meliputi pembersihan data dan label encoding, kemudian dataset dibagi dengan rasio 80:20, dan SMOTE diterapkan eksklusif pada data pelatihan. Hasil pengujian menunjukkan model mencapai accuracy 77,15%, precision 55,65%, recall 68,45%, dan F1-score 61,39%. Analisis feature importance mengungkapkan bahwa Contract, OnlineSecurity, dan TechSupport merupakan faktor paling dominan yang memengaruhi churn. Integrasi Random Forest dan SMOTE terbukti menghasilkan model prediksi yang andal sekaligus memberikan wawasan strategis bagi perusahaan dalam merancang program retensi pelanggan yang lebih efektif.

Analisis Sentimen Headline CNBC Indonesia Menggunakan Multinomial Logistic Regression dan TF-IDF Muhammad Yusra; Roberto Kaban
Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Vol. 3 No. 4 (2026): SINTA: JULI (special issue Machine Learning)
Publisher : Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/sinta.v3i4.353

Penelitian ini mengajarkan penerapan Multinomial Logistic Regression dengan representasi fitur Term Frekuensi Inverse Document Frekuensi (TF-IDF) untuk mengklasifikasikan sentimen pada headline berita pasar keuangan Indonesia. Dataset penelitian mencakup 9.819 headline berita CNBC Indonesia yang dipublikasikan selama Januari 2024 hingga Maret 2025dan dianotasi secara manual ke dalam tiga kategori sentimen, yaitu positif (29,4%), netral (44,4%), dan negatif (26,2%). Representasi teks dibangun menggunakan TF-IDF dengan ukuran kosakata maksimal sebanyak 5.000 kata. Berdasarkan hasil evaluasi, model memperoleh akurasi sebesar 82,43%, dengan nilai macro perception 0,8200, macro recall 0,8233, dan macro F1-score 0,8200. Pengujian menggunakan skema 5-fold cross-validation menghasilkan rata-rata akurasi 82,04%, yang menunjukkan bahwa model memiliki kemampuan generalisasi yang baik. Analisis koefisien menunjukkan bahwa istilah anjlok dan ambruk berkontribusi terhadap prediksi sentimen negatif, sedangkan borong dan melesat menjadi indikator sentimen positif. Kesalahan klasifikasi terutama ditemukan pada headline yang mengandung ambiguitas konteks. Oleh karena itu, penelitian selanjutnya dapat memanfaatkan pendekatan berbasis transformator untuk meningkatkan pemahaman konteks dan akurasi klasifikasi sentimen.

Earthquake Detection and Tsunami Disaster Management Using Vibration Sensors Jihan Nadirah Simatupang; Fauziah; Fitri Ramadhani Pane; M. Irfan Affandi; Roberto Kaban; Surizar Rahmi Danur
JCEIT: Journal of Computer Engineering and Information Technology Vol. 1 No. 3: JCEIT: Journal of Computer Engineering and Information Technology (July 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v1i3.16

An earthquake is a vibration or tremor that occurs on the Earth's surface due to a sudden release of energy from within that creates seismic waves. The frequency of an area refers to the type and size of earthquakes experienced over a period of time. Along with the development of earthquake detection system technology provides a solution to minimize the impact of earthquake events. Natural disasters that often occur in the country of Indonesia, one of the natural disasters that often occur is earthquakes. And many people do not know when an earthquake will come. So an earthquake detection tool was made with Arduino Uno which is a tool that can detect earthquake vibrations. With this tool using a vibration sensor sensor that can detect vibrations. REFERENCES Agustanti, Sri Primaini, Hartini Hartini, Nurhayani Nurhayani, and Dimas Dibya Hartanto. 2022. “Aplikasi Mikrokontroler Arduino Uno Dalam Rancang Bangun Kunci Pintu Menggunakan E-Ktp.” Jusikom : Jurnal Sistem Komputer Musirawas 7(1):74–88. doi: 10.32767/jusikom.v7i1.1611. Al-Ali, A. R., Beheiry, S., Alnabulsi, A., Obaid, S., Mansoor, N., Odeh, N., & Mostafa, A. (2024). An IoT-Based Road Bridge Health Monitoring and Warning System. Sensors, 24(2), 469. https://doi.org/10.3390/s24020469 Ayuningtyas, D., Windiarti, S., Hadi, M. S., Fasrini, U. U., & Barinda, S. (2021). Disaster Preparedness and Mitigation in Indonesia: A Narrative Review. Iranian Journal of Public Health. https://doi.org/10.18502/ijph.v50i8.6799 Basid, A., Mahardika, I. K., Subchan, W., & Astutik, S. (2021a). Mapping risk levels of earthquake damage as disaster mitigation efforts: Case studies in West Java, Central Sulawesi and Lombok. 040007. https://doi.org/10.1063/5.0037540 Chen, H., Li, G., Fang, R., & Zheng, M. (2021). Early Warning Indicators of Landslides Based on Deep Displacements: Applications on Jinping Landslide and Wendong Landslide, China. Frontiers in Earth Science, 9, 747379. https://doi.org/10.3389/feart.2021.747379 Clements, T. (2023). Earthquake Detection with tinyML. Seismological Research Letters. https://doi.org/10.1785/0220220322 Dhira, Y., Meilano, I., & Dudy, D. W. (2021). Analysis of Tectonic Plate Velocity Variations in the Sunda Strait Based on GPS Time-series Data. IOP Conference Series: Earth and Environmental Science, 873(1), 012084. https://doi.org/10.1088/1755-1315/873/1/012084 Esposito, M., Palma, L., Belli, A., Sabbatini, L., & Pierleoni, P. (2022a). Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review. Sensors, 22(6), 2124. https://doi.org/10.3390/s22062124 Fajri, A. H., Murti, M. A., & Priramadhi, R. A. (2021). Design of earthquake early warning system based OMRON D7S vibrate sensor. IOP Conference Series: Materials Science and Engineering, 1098(4), 042099. https://doi.org/10.1088/1757-899X/1098/4/042099 Fuady, M., Munadi, R., & Fuady, M. A. K. (2021). Disaster mitigation in Indonesia: Between plans and reality. IOP Conference Series: Materials Science and Engineering, 1087(1), 012011. https://doi.org/10.1088/1757-899X/1087/1/012011 Gupta, T., & Roy, S. (2024). Applications of Artificial Intelligence in Disaster Management. Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence, 313–318. https://doi.org/10.1145/3669754.3669802 Harahap, Partaonan, Benny Oktrialdi, and Rahmad Fauzi Siregar. 2023. “Implementasi Alat Pendeteksi Gempa Berbasis Arduino Uno Dengan Memanfaatkan Sensor Getar (Vibration).” Seminar Nasional Teknik Elektro. Howard, S. K., Schrum, L., Voogt, J., & Sligte, H. (2021). Designing research to inform sustainability and scalability of digital technology innovations. Educational Technology Research and Development, 69(4), 2309–2329. https://doi.org/10.1007/s11423-020-09913-y Ira, A. P., & Purwantara, S. (2021). School Community Preparedness in Anticipation of Earthquake and Tsunami Threats in Temon Sub-District. IOP Conference Series: Earth and Environmental Science, 884(1), 012046. https://doi.org/10.1088/1755-1315/884/1/012046 Köhli, M., Weimar, J., Schmidt, S., Schmidt, F. P., Lambertz, A., Weber, L., Kaminski, J., & Schmidt, U. (2024). Arduino-Based Readout Electronics for Nuclear and Particle Physics. Sensors, 24(9), 2935. https://doi.org/10.3390/s24092935 Komarizadehasl, S., Mobaraki, B., Ma, H., Lozano-Galant, J.-A., & Turmo, J. (2021). Development of a Low-Cost System for the Accurate Measurement of Structural Vibrations. Sensors, 21(18), 6191. https://doi.org/10.3390/s21186191 Laia, Firdaus, Tobias Duha, Mitranikasih Laia, Amirudin Khorul Huda, and Agung Jasuma. 2023. “Klasifikasi Data Gempa Bumi Di Pulau Sumatera Menggunakan Algoritma Naïve Bayes.” Jurnal Informatika 2(1):23–27. doi: 10.57094/ji.v2i1.840. Liu, T., Zhang, M., Li, Z., Dou, H., Zhang, W., Yang, J., Wu, P., Li, D., & Mu, X. (2025). Machine learning-assisted wearable sensing systems for speech recognition and interaction. Nature Communications, 16(1), 2363. https://doi.org/10.1038/s41467-025-57629-5 Mamahit, Calvin. 2024. “Rumah Pintar Dengan Lampu Kontrol Suara Menggunakan Arduino Uno R3.” Electrician : Jurnal Rekayasa Dan Teknologi Elektro 18(2):144–52. doi: 10.23960/elc.v18n2.2567. Mar’atuzzulfa, Salma, Rastri Prathivi, and Susanto. 2025. “Klasifikasi Gempa Bumi Berdasarkan Magnitudo Menggunakan Metode Logistic Regression.” KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) 6(1):227–34. Martin, K., and D. Susandi. 2022. “Perancangan Dan Implementasi Sistem Irigasi Kabut Otomatis Tanaman Edelweis Menggunakan Mikrokontroler Arduino Uno.” Jurnal IKRA-ITH INFORMATIKA 6(103):57–66. McBride, S. K., Smith, H., Morgoch, M., Sumy, D., Jenkins, M., Peek, L., Bostrom, A., Baldwin, D., Reddy, E., De Groot, R., Becker, J., Johnston, D., & Wood, M. (2022). Evidence-based guidelines for protective actions and earthquake early warning systems. GEOPHYSICS, 87(1), WA77–WA102. https://doi.org/10.1190/geo2021-0222.1 Nagasa, M. M., & Johnson, P. L. D. (2025). Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network. Sensors, 25(8), 2348. https://doi.org/10.3390/s25082348 Patel, S. C., & Allen, R. M. (2022). The MyShake App: User Experience of Early Warning Delivery and Earthquake Shaking. Seismological Research Letters, 93(6), 3324–3336. https://doi.org/10.1785/0220220062 Ramdhan, M., Palgunadi, K. H., Mukti, M. M., Librian, V., Daniarsyad, G., Muttaqy, F., Hidayat, E., Syuhada, S., Hanif, M., Mursitantyo, A., Lühr, B.-G., Nugraha, A. D., Widiyantoro, S., Setyonegoro, W., & Febriani, F. (2025). Aftershock sequence of the Yogyakarta earthquake 2006 (Mw ~ 6.4), Indonesia, based on analysis of hypocenter relocation, static, and dynamic stress. Natural Hazards. https://doi.org/10.1007/s11069-025-07440-8 Romanssini, M., De Aguirre, P. C. C., Compassi-Severo, L., & Girardi, A. G. (2023). A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery. Eng, 4(3), 1797–1817. https://doi.org/10.3390/eng4030102 Sekine, K., & Hayakawa, K. (2022). Development of Vibration Measurement System using a Microcontroller. EPI International Journal of Engineering, 5(2), 98–103. https://doi.org/10.25042/epi-ije.082022.04 Setiawan, B., Rizal, M., Yunita, H., Saidi, T., Hasan, M., & Zulkifli, Z. (2022). Validating a low-cost seismometer using a shaking table. E3S Web of Conferences, 340, 02009. https://doi.org/10.1051/e3sconf/202234002009 Silalahi, Andri, Deddy Hartama, Ika Okta Kirana, Indra Gunawan, and Sumarno Sumarno. 2022. “Rancang Bangun Alat Pendeteksi Kebocoran Pada Tabung Gas Menggunakan Arduino Berbasis Sms.” Jurnal Krisnadana 1(3):48–58. doi: 10.58982/krisnadana.v1i3.178. Simanjuntak, T., & Ririmasse, M. (2021). Archaeology of disaster in Indonesia: Where are we now? Berita Sedimentologi, 47(3), 17–21. https://doi.org/10.51835/bsed.2021.47.3.351 Sinaga, G. H. D., Loeqman, A., Siagian, R. C., & Sinaga, M. P. (2022). Analysis of Coulomb Stress Changes in Aceh Earthquake on Sibayak Volcano. Jurnal Pendidikan Fisika Dan Teknologi, 8(2), 217–227. https://doi.org/10.29303/jpft.v8i2.4409 Tansa, Salmawaty, Nur’aeni Latekeng, Raghel Yunginger, and Iskandar Z. Nasibu. 2024. “Monitoring Kualitas Air Sungai (Kekeruhan, Suhu, TDS,PH) Menggunakan Mikrokontroler Atmega328.” Jambura Journal of Electrical and Electronics Engineering 6(1):70–75. doi: 10.37905/jjeee.v6i1.23315. Tian, B., Liu, W., Mo, H., Li, W., Wang, Y., & Adhikari, B. R. (2023). Detecting the Unseen: Understanding the Mechanisms and Working Principles of Earthquake Sensors. Sensors, 23(11), 5335. https://doi.org/10.3390/s23115335 Witjaksana, Budi, and Andi Syaiful Amal. 2021. “Penerapan Sensor Pedeteksi Dini Gempa Bumi Pada Bangunan Konstruksi.” Seminar Keinsinyuran Program Studi Program Profesi Insinyur 1(1):26–32. doi: 10.22219/skpsppi.v1i0.4216. Zhukovsky, V., Rusu, I., Zavoloka, M., & Grynyova, I. (2021). The concept of an automatic system for preliminary entry of hazardous facility into emergency mode with an approaching earthquake. IOP Conference Series: Materials Science and Engineering, 1141(1), 012023. https://doi.org/10.1088/1757-899X/1141/1/012023

Studi Literatur Information Retrieval System Semantik Untuk Pencarian Produk E-Commerce Maulana Farras; Silvia Hanum; Roberto Kaban
LOFIAN: Jurnal Teknologi Informasi dan Komunikasi Vol 5 No 2 (2026): Pebruari
Publisher : Universitas Mandiri Bina Prestasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58918/lofian.v5i2.291

This research aims to analyze the development of the Semantic Information Retrieval System (Semantic IRS) approach in e-commerce product search based on descriptions through a literature study of 15 scientific articles consisting of national and international publications. The results of the analysis show that 33% of articles use the semantic IR and dense retrieval approaches as the basis for semantic mapping between queries and product documents. The late interaction and multimodal semantic retrieval approaches were each applied in 27% of articles, indicating an increasing research focus on token-level semantic interaction modeling and the integration of textual and visual information. Additionally, 13% of articles utilized query expansion and semantic relation modeling as supporting methods to improve search relevance. In terms of methodology, 80% of article used a quantitative experimental approach with information retrieval system metric-based evaluation, and 67% of articles adopted neural models. Overall, these quantitative findings indicate that neural model-based Semantic IR, late interaction, and multimodal approaches are the dominant and most relevant directions for handling long and unstructured description-based product searches in modern e-commerce systems.

Analisis Sentimen Publik Terhadap RUU KUHAP di Platform X Menggunakan Metode TF-IDF dan Naïve Bayes Junaidy; Muhammad Fauzan; Roberto Kaban
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.518

The rapid development of social media has established Platform X as one of the primary channels for the public to express opinions on public policy issues, including the Draft Criminal Procedure Code (RUU KUHAP). This study aims to analyze public sentiment toward the RUU KUHAP based on tweet data collected from Platform X. A total of 2,273 valid data points were obtained and utilized in this research. The selected data underwent several preprocessing stages, including case folding, cleansing, tokenizing, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, while the sentiment classification process employed the Multinomial Naive Bayes algorithm, with the dataset split into training and testing sets. Model performance was evaluated using a confusion matrix alongside precision, recall, and F1-score metrics. The results indicate that public sentiment toward the RUU KUHAP is dominated by negative sentiment at 45.5%, followed by neutral sentiment at 32.0%, and positive sentiment at 22.5%. Performance evaluation shows that for the negative class, the model achieved a precision of 0.71, recall of 0.93, and F1-score of 0.80. For the neutral class, the precision was 0.74, recall 0.44, and F1-score 0.55, while the positive class reached a precision of 0.85, recall 0.80, and F1-score 0.82. Overall, the model achieved an accuracy of 74.07%, demonstrating that the application of TF-IDF and Naïve Bayes is effective in classifying public sentiment, despite persistent limitations in identifying neutral sentiment.

Klasifikasi Penyakit Diabetes Menggunakan Algoritma K-Nearest Neighbor (KNN) Nurdilla Nurdilla; Roberto Kaban
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 2 (2026): July
Publisher : Subaltren Inti Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Diabetes mellitus is a chronic metabolic disease characterized by high blood glucose levels and has the potential to cause various complications if not detected early. The use of machine learning technology is increasingly developing in the health sector because it can help the process of analyzing and classifying diseases based on patient data. This study aims to apply the K-Nearest Neighbor (KNN) algorithm to classify diabetes using the Pima Indians Diabetes Dataset. The dataset used consists of 768 patient data with 8 predictor attributes and 1 target attribute. The research stages include data cleaning and improvement through preprocessing, data normalization using the Min-Max Scaling method, dividing the dataset into training data and testing data with a ratio of 80:20, and the application of the KNN algorithm with a K value of 5. Model performance evaluation was carried out using a Confusion Matrix which produces Accuracy, Precision, Recall, and F1-Score values. Based on the test results, the model obtained Accuracy of 74.68%, Precision of 66.00%, Recall of 60.00%, and F1-Score of 62.86%. These results demonstrate that the KNN algorithm is capable of classifying diabetes data with fairly good performance based on available health attributes. This research is expected to serve as a reference in the development of machine learning-based decision support systems to aid in the identification of diabetes.

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.

KLASIFIKASI RISIKO KESEHATAN MENTAL GENERASI Z BERDASARKAN PENGGUNAAN MEDIA SOSIAL MENGGUNAKAN METODE NAÏVE BAYES Laila Syafira; Zahwa Aura Mutiara Hsb; Roberto Kaban
JURNAL ILMIAH RESEARCH STUDENT Vol. 3 No. 2 (2026): September
Publisher : KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jirs.v3i2.11755

Kesehatan mental Generasi Z menjadi perhatian penting karena intensitas penggunaan media sosial yang tinggi berpotensi memengaruhi kondisi psikologis, seperti kecemasan, depresi, dan gangguan tidur. Penelitian ini bertujuan mengklasifikasikan risiko kesehatan mental Generasi Z berdasarkan pola penggunaan media sosial menggunakan algoritma Naïve Bayes. Dataset Social Media and Mental Health dari Kaggle digunakan sebanyak 481 data responden, kemudian difilter pada rentang usia 18–27 tahun sehingga diperoleh 356 data. Tahap praproses meliputi pelabelan, penanganan missing value, penyeimbangan data, transformasi data, normalisasi, dan seleksi fitur sehingga diperoleh 312 data final dengan 10 atribut terpilih. Data dibagi menjadi 70% data latih dan 30% data uji. Hasil pengujian menunjukkan akurasi sebesar 92,55%, presisi 91,67%, dan recall 93,62%. Hasil ini menunjukkan bahwa pola penggunaan media sosial berpotensi digunakan sebagai indikator awal dalam deteksi risiko kesehatan mental Generasi Z.

Co-Authors Ainun Aminah Alexander Silitonga Andara Cantika Effendy Angga Pradana Asprina Br. Surbakti Darwin Panjaitan David Jumpa Malem Sembiring Egi Mutiara Br Sitepu Eriansyah Saputra H Fahmil Triaga Fajrillah, Fajrillah Fandy Syahputra Fauziah Fitri Ramadhani Pane Humaira Humaira Ita Margaretta Tarigan Jihan Nadirah Simatupang Junaidy Laila Syafira M. Irfan Affandi Mahdianta Pandia Maulana Farras Meiliyani Br Ginting Muhammad Fauzan Muhammad Junaidi Muhammad Rizky Fajar Muhammad Yusra Nurdilla Nurdilla Nurhafiz Ahmad Rangkuti Nuzaima Agustari Poltak Sihombing Purwanto Simamora Rizky Adha Safarul Ilham Silvia Hanum Suata Wan Kelispa Halawa Surizar Rahmi Danur Tri Indah Prasasti Wan Saddam Hasan Yudi Abdul Halim Zahwa Aura Mutiara Hsb Zuhaira Agustari

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