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Prediksi dan Deteksi Bug pada Visual Studio Code menggunakan Algoritma Naive Bayes Siti Hardianti; Roberto Kaban
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 3 (2026): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i3.953

Software quality is a critical aspect of modern software engineering. One of the primary challenges developers face is the early detection of bugs before software is released into the production environment. This study develops a bug prediction model using the Naive Bayes algorithm applied to the JM1 dataset from NASA's Metrics Data Program, sourced from Kaggle. The JM1 dataset consists of source code metrics from a NASA project, comprising 10,885 modules with 21 numerical features that include Halstead and McCabe metrics. All experimental stages were conducted using Python with the Pandas, Scikit-learn, NumPy, and Matplotlib libraries. Experimental results show that the Naive Bayes model achieved an accuracy of 79.93%, an ROC-AUC value of 0.6761, precision of 46.26%, recall of 23.52%, and an F1-Score of 31.18%. These findings indicate that while Naive Bayes effectively identifies non-defective modules, it faces challenges in detecting defective modules due to significant class imbalance (80.65% vs. 19.35%). The contributions of this study include an in-depth analysis of the impact of class imbalance on bug prediction performance, as well as recommendations for handling techniques such as SMOTE and ensemble learning to improve future performance.

Implementasi Naive Bayes untuk Memprediksi Prestasi Belajar Siswa MTs Fathurrahman Padang Tualang Alfin Noval Permana; Juwita Adinda; Roberto Kaban
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1698

Student learning achievement is an important indicator in evaluating the success of the learning process in madrasah. This study aims to implement the Naive Bayes algorithm in predicting the learning achievement of Grade VII, VIII, and IX students at MTs Fathurrahman Padang Tualang. The research data uses 77 students from three grade levels; 38 students (49.35%) are classified as Achieving and 39 students (50.65%) as Underachieving. Model evaluation using the Hold-Out Split Data method (80% training, 20% testing) achieved Accuracy of 93.75%, Precision of 100.00%, and Recall of 87.50%, confirming the model's high reliability. The UAS variable is the strongest predictor with a mean difference of 13.59 points between classes (μAchieving = 80.92 vs μUnderachieving = 67.33). This research proves that Naive Bayes is an effective and efficient classification algorithm for predicting student learning achievement across grade levels in madrasah tsanawiyah. The proposed model can support educators in identifying students with potential academic difficulties, enabling early intervention and more targeted learning strategies. Furthermore, the implementation of predictive analytics provides valuable insights for improving academic management and supporting data-driven decision-making in educational institutions.

Prediksi Tingkat Pemahaman Siswa Smp Swasta Tenera Berdasarkan Aktivitas Belajar Menggunakan Naive Bayes M. Dimas Prayoga; Mona Ayunda; Roberto Kaban
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1699

This study aims to develop a prediction model for determining the comprehension level of students at SMP Swasta Tenera using the Naive Bayes algorithm based on academic learning activities. The prediction model utilizes student learning data, including subject grades and attendance records, as the main variables to classify students’ comprehension levels. The data used in this study were collected from 90 students consisting of Grade VII (28 students), Grade VIII (23 students), and Grade IX (39 students), covering performance data from 10 subjects. The research method applies a quantitative approach with data processing and classification analysis using the Naive Bayes algorithm. The evaluation results show that the developed model achieved an accuracy level of 83.33%, with a precision value of 78.12%, recall of 98.04%, and an F1-score of 86.96%. The prediction results indicate that Grade VII students have the lowest comprehension level at 42.9%, followed by Grade VIII at 60.9% and Grade IX at 64.1%. This research demonstrates that machine learning-based prediction systems can support educational decision-making by identifying students who require learning assistance and enabling schools to implement faster, more targeted, and effective academic interventions.

Klasifikasi Sentimen Pengguna Terhadap Pembayaran Digital Menggunakan Algoritma Naive Bayes Nayla Syafiah Asmi; -, Sinta Amellyya Sari; Roberto Kaban
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1713

Perkembangan teknologi finansial (fintech) di Indonesia telah mendorong meningkatnya penggunaan aplikasi pembayaran digital, salah satunya DANA. Banyaknya ulasan pengguna pada Google Play Store menghasilkan data tekstual yang dapat dimanfaatkan untuk mengetahui persepsi pengguna terhadap kualitas layanan aplikasi. Penelitian ini bertujuan untuk mengklasifikasikan sentimen pengguna aplikasi DANA menggunakan algoritma Naïve Bayes. Dataset yang digunakan berasal dari Kaggle dengan jumlah 50.000 ulasan yang telah dikategorikan ke dalam tiga kelas sentimen, yaitu positif, negatif, dan netral. Tahapan penelitian meliputi pengumpulan data, preprocessing teks (case folding, cleaning, tokenizing, stopword removal, dan stemming), pembobotan kata menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF), proses klasifikasi menggunakan algoritma Multinomial Naïve Bayes, serta evaluasi model menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model memperoleh nilai accuracy sebesar 79,26%. Kelas sentimen positif memiliki performa terbaik dengan nilai precision sebesar 0,85, recall 0,94, dan F1-score 0,89, sedangkan kelas sentimen netral memiliki nilai recall terendah sebesar 0,19 akibat ketidakseimbangan distribusi data. Berdasarkan hasil tersebut, algoritma Naïve Bayes mampu memberikan kinerja yang cukup baik dalam mengklasifikasikan sentimen ulasan pengguna aplikasi DANA dan dapat dimanfaatkan sebagai salah satu metode analisis opini pengguna untuk mendukung peningkatan kualitas layanan aplikasi pembayaran digital.

The Effect of Social Media Use on Student Learning Motivation at Era Utama Pancur Batu High School Modesta Ginting; David JM Sembiring; Roberto Kaban; Nurhafiz Ahmad Rangkuti
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 Morina Buulolo; Carles Wiranto Laia; Nafaoli Buulolo; Roberto Kaban; Rimmar Siringo-ringo
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

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