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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional Al Ishlah Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) IT JOURNAL RESEARCH AND DEVELOPMENT Rang Teknik Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Kaputama (JIK) Jurnal Teknologi Informasi dan Pendidikan Informatika INFOKUM Syntax: Journal of Software Engineering, Computer Science and Information Technology Instal : Jurnal Komputer Jurnal Pengabdian Masyarakat IPTEK Bulletin of Information Technology (BIT) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Minfo Polgan (JMP) Journal of Computer Science and Information Systems (JCoInS) Jurnal Nasional Teknologi Komputer Jurnal Abdimas Hawari Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Masyarakat Indonesia DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY IPTEK: Jurnal Hasil Pengabdian kepada Masyarakat Indonesian Journal of Education And Computer Science Jurnal INFOTEL Jurnal Pengabdian Masyarakat Berdampak
Khairul
Universitas Pembangunan Panca Budi
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ANALISIS ALGORITMA K-MEANS CLUSTERING DALAM IDENTIFIKASI TINGKAT RISIKO PENYAKIT BERDASARKAN DATA REKAM MEDIS PASIEN Wina Aulia; Andysah Putera Utama Siahaan; Leni Marlina; Khairul Khairul; Muhammad Iqbal
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3551

Abstract

Abstract: This study aims to classify patients' health conditions based on six indicators: systolic blood pressure, diastolic blood pressure, fasting blood glucose, normal blood glucose, cholesterol level, and uric acid level using the K-Means Clustering method. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, which resulted in five as the optimal number of clusters. The results show that the manual approach produces a more stable distribution that closely aligns with the clinical interpretation of cluster categories: Healthy (C1), Safe (C2), Alert (C3), Moderate (C4), and Severe (C5). Visualization was performed for each indicator through scatter plots and color mapping against normal value thresholds, aiding in the understanding of the distribution of patient conditions across clusters. The analysis reveals that even if a patient has one or more indicators within normal limits, they are not automatically classified into the Healthy or Safe clusters. Discrepancies in other indicators can place them in higher-risk clusters such as Alert, Moderate, or Severe. Therefore, this clustering approach provides a comprehensive view of health conditions based on a combination of features, rather than a single parameter. This research is useful in supporting early diagnosis and data-driven decision-making processes and can be integrated into health information systems for automatic risk classification of patient populations. Keywords: K-Means, Clustering, Health, Blood Pressure, Blood Glucose, Cholesterol, Uric Acid, Data Visualization Abstrak: Penelitian ini bertujuan untuk mengelompokkan kondisi kesehatan pasien berdasarkan enam indikator, yaitu tekanan darah sistolik, tekanan darah diastolik, kadar gula puasa, kadar gula normal, kadar kolesterol, dan kadar asam urat menggunakan metode K-Means Clustering. Penentuan jumlah klaster optimal dilakukan dengan metode Elbow dan Silhouette Score, yang menghasilkan lima klaster sebagai jumlah optimal. Hasil menunjukkan bahwa pendekatan manual menghasilkan distribusi yang lebih stabil dan mendekati pemaknaan klinis dari kategori klaster, yaitu: Sehat (C1), Aman (C2), Waspada (C3), Sedang (C4), dan Berat (C5). Visualisasi dilakukan untuk setiap indikator melalui scatter plot dan pemetaan warna terhadap batas nilai normal, yang membantu dalam memahami sebaran kondisi pasien pada masing-masing klaster. Hasil analisis menunjukkan bahwa meskipun seorang pasien memiliki satu atau lebih indikator dalam batas normal, tidak secara otomatis tergolong dalam klaster Sehat atau Aman. Ketidaksesuaian pada indikator lainnya dapat menempatkan pasien ke dalam klaster yang lebih tinggi risikonya, seperti Waspada, Sedang, atau Berat. Oleh karena itu, pendekatan clustering ini memberikan gambaran menyeluruh terhadap kondisi kesehatan berdasarkan kombinasi fitur, bukan hanya pada satu parameter. Penelitian ini bermanfaat untuk mendukung proses diagnosis awal dan pengambilan keputusan berbasis data, serta dapat diintegrasikan dalam sistem informasi kesehatan untuk klasifikasi risiko populasi pasien secara otomatis.Kata kunci: K-Means, Clustering, Kesehatan, Tekanan Darah, Gula Darah, Kolesterol, Asam Urat, Visualisasi Data
RANCANG BANGUN SISTEM MANAJEMEN LAYANAN BENGKEL RAHMAT97 AUDIO PROJECT BERBASIS WEB Tri Cahya Agung; Khairul Khairul; Jodi Hendrawan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4202

Abstract

Abstract: Rahmad97 Audio is a car audio workshop that offers installation and sales services for audio systems. To improve service management and operational efficiency, a computerized web-based information system is needed. This study aims to design and develop a web-based service management system that facilitates the management of customer data, product inventory, transactions, and reports in an integrated manner. The system development follows the Waterfall model, consisting of requirements analysis, design, implementation, and testing stages.The implementation results show that the system significantly streamlines service and transaction recording processes, reduces manual entry errors, and enables real-time monitoring of workshop activities. The system includes multi-role login (admin, owner, and sales) to ensure data security and access control based on user roles. This web-based system helps the workshop manage its services more efficiently, in a structured and organized way, making it easier for business owners to monitor operations. Keywords: Information System, Car Audio Workshop, Service Management, Web-Based,                 PHP, MySQL Abstrak: Bengkel Rahmad97 Audio merupakan usaha jasa pemasangan dan penjualan perangkat audio mobil yang membutuhkan sistem informasi terkomputerisasi untuk mengelola layanan secara efektif. Penelitian ini bertujuan untuk merancang dan membangun sistem manajemen layanan bengkel berbasis web yang dapat membantu dalam pengelolaan data pelanggan, produk, transaksi, serta pelaporan secara terintegrasi. Metode pengembangan sistem yang digunakan adalah model Waterfall, yang meliputi tahapan analisis kebutuhan, perancangan, implementasi, dan pengujian. Hasil dari implementasi menunjukkan bahwa sistem ini mampu mempercepat proses pencatatan layanan dan transaksi, meminimalkan kesalahan pencatatan manual, serta memberikan kemudahan dalam monitoring aktivitas bengkel secara real-time. Sistem juga dilengkapi dengan fitur login multi-role (admin, owner, dan sales) untuk menjaga keamanan dan pembatasan akses data sesuai peran. Dengan adanya sistem ini, pengelolaan layanan bengkel menjadi lebih efisien, terstruktur, dan mudah diawasi oleh pemilik usaha. Kata kunci: Sistem Informasi, Bengkel Audio Mobil, Manajemen Layanan, Web, PHP,                    MySQL
ANALISIS DETEKSI FENOMENA BRAIN ROT PADA MAHASISWA MENGGUNAKAN METODE RANDOM FOREST Feby Wulandari Sembiring; Arip Muhridan; Mhd Ihsan Abidi; Irfan Abadi Saragih; Khairul
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6080

Abstract

Abstract: The phenomenon of brain rot poses a serious threat to the decline of students' cognitive function due to excessive exposure to low-quality digital content. This study aims to analyze the severity of brain rot and identify the most dominant digital behavioral factors of this phenomenon among students. As a solution to predict the level of risk quantitatively, this study implemented a machine learning approach using the Random Forest Regressor method. Data were collected from 500 student respondents through observation and questionnaires covering variables such as scrolling duration, app switching, GPA, study time, and cognitive symptoms. The test results showed that the model has not achieved optimal performance with an R2-Score of -0.177, RMSE 35.41, and MAE 31.1661. The low accuracy was influenced by inconsistencies in input data units and weak feature correlation in capturing non-linear patterns in the dataset. The study concluded that although scrolling duration was identified as the main influencing factor, the Random Forest model experienced high bias (underfitting). Therefore, hyperparameter optimization and data quality improvement are needed for future use. Keyword: brainrot; students; machine learning; random forest regressor; digital behavior.     Abstrak: Fenomena brainrot (pembusukan otak) menjadi ancaman serius bagi penurunan fungsi kognitif mahasiswa akibat paparan konten digital yang berlebihan dan tidak berkualitas. Penelitian ini bertujuan untuk menganalisis tingkat keparahan brainrot serta mengidentifikasi faktor perilaku digital yang paling mendominasi fenomena tersebut pada kalangan mahasiswa. Sebagai solusi untuk memprediksi tingkat risiko secara kuantitatif, penelitian ini mengimplementasikan pendekatan machine learning dengan metode Random Forest Regressor. Data dikumpulkan dari 500 responden mahasiswa melalui observasi dan kuesioner yang mencakup variabel durasi scrolling, app switching, IPK, lama waktu belajar, dan gejala kognitif. Hasil pengujian menunjukkan bahwa model belum mencapai performa optimal dengan nilai R2-Score sebesar -0,177, RMSE 35,41, dan MAE 31,1661. Rendahnya akurasi dipengaruhi oleh ketidakkonsistenan satuan data input serta korelasi fitur yang kurang kuat dalam menangkap pola non-linear pada dataset. Simpulan penelitian menunjukkan bahwa meskipun durasi scrolling teridentifikasi sebagai faktor pengaruh utama, model Random Forest mengalami high bias (underfitting) sehingga diperlukan optimasi hyperparameter dan penyempurnaan kualitas data untuk penggunaan di masa mendatang.. Kata kunci: brainrot; mahasiswa; machine learning; random forest regressor; perilaku digital.  
Klasterisasi Pola Curah Hujan Berdasarkan Data Alat Pengamatan Menggunakan Hierarchical Cluster Analysis (Studi Kasus: BMKG Wilayah Sumatera Utara) Edy Sarwono Ponco; Zulham Sitorus; Khairul
Jurnal Nasional Teknologi Komputer Vol 6 No 4 (2026): Oktober 2026
Publisher : CV. Hawari

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Abstract

The planning of hydrometeorological disaster mitigation, water resource management, agriculture, and regional spatial planning is highly dependent on the weather. The diverse geographical conditions of North Sumatra can cause significant variations in rainfall patterns between regions. The objective of this research is to cluster rainfall patterns based on BMKG observation data in the North Sumatra region. This is done using the hierarchical cluster analysis method. The stages of data preprocessing, standardisation, distance measurement between objects, dendrogram formation, and determination of the number of clusters are used to analyse rainfall data to systematically identify the similarities in rainfall characteristics between observation stations. The clustering results show that areas are grouped based on similar rainfall patterns, with each cluster representing different levels of rainfall. These results can help us understand the distribution of rainfall in North Sumatra and assist in making decisions about climatology, disaster mitigation, and regional planning. This study shows that hierarchical cluster analysis can be used as an analytical method to accurately group rainfall patterns based on meteorological observation data.
Optimasi Klasifikasi Kerawanan Gempa Bumi di Wilayah Sumatra Utara Berbasis Spatio-Temporal AutoML dengan Bayesian Optimization Menggunakan Data Historis Seismik Albertus Tua Simanullang; Khairul; Zulham Sitorus
Jurnal Nasional Teknologi Komputer Vol 6 No 4 (2026): Oktober 2026
Publisher : CV. Hawari

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Abstract

Earthquakes are one of the most uncertain geological disasters and have the potential to cause significant damage in North Sumatra. The pattern of earthquake occurrences is influenced by complex tectonic conditions, which are affected by changes in seismic activity over time and the characteristics of the location. The aim of this research is to develop a spatiotemporal-based AutoML earthquake vulnerability classification model optimised with Bayesian optimisation methods. The seismic data used includes epicentre coordinates, depth, magnitude, time, frequency, and distance from active earthquake sources. Data preprocessing, spatial and temporal pattern analysis, feature engineering, vulnerability class determination, training several classification algorithms through an automated machine training framework, and hyperparameter optimisation using Bayesian optimisation. To assess the model's performance, accuracy, precision, recall, F1-score, area under the curve, and confusion matrix metrics are used. To reduce the possibility of bias and ensure that the model can be generalised to various locations and periods of occurrence, spatial and temporal validation are used. It is expected that the research results will produce a classification model with higher accuracy and stability than conventional classification methods. Next, the best model is used to divide the area into low, medium, and high vulnerability categories. It is hoped that this research will help develop a more adaptive, objective, and efficient data-based earthquake vulnerability mapping system that will assist in the decision-making process and disaster mitigation planning in the North Sumatra region.
Klasifikasi Potensi Bencana Ekstrem Hidrometeorologi di Kota Medan Menggunakan Algoritma Random Forest Dengan Teknik Purposive Sampling Indra Wadiasto; Khairul; Zulham Sitorus
Jurnal Nasional Teknologi Komputer Vol 6 No 4 (2026): Oktober 2026
Publisher : CV. Hawari

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Abstract

One of the threats that can endanger society is extreme hydrometeorological disasters, especially in cities with high construction activity and population density. Due to weather changes and local environmental characteristics, Medan City, one of the major cities in Indonesia, is vulnerable to hydrometeorological events such as floods, extreme rainfall, strong winds, and puddles. This study uses the Random Forest algorithm and purposive sampling technique to classify the likelihood of extreme hydrometeorological disasters in the city of Medan. The data used is based on specific criteria relevant to disaster potential indicators, such as topographic conditions, land use, population density, and rainfall. The Random Forest method is used because it can process data with many variables and make accurate classifications through the combination of several decision trees. The research results are expected to classify the areas of Medan City based on their disaster potential: low, medium, or high. This category can be used as a basis for spatial planning, mitigation strategies, and decisions made by the government and relevant parties to reduce the risk of hydrometeorological disasters in the City of Medan.
Klasifikasi Pola Iklim Bulanan di Sumatera Utara Menggunakan Jaringan Syaraf Tiruan Multilayer Perceptron (MLP) Pada Historis Dataset BMKG Sriwahyuni; Khairul; Rian Farta Wijaya
Jurnal Nasional Teknologi Komputer Vol 6 No 4 (2026): Oktober 2026
Publisher : CV. Hawari

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Abstract

Based on historical data from BMKG, the Multilayer Perceptron Neural Network (MLP) is used in this study to classify monthly climate patterns in North Sumatra. A machine learning approach is necessary to identify more accurate patterns due to the differences in nonlinear climate elements such as rainfall, temperature, humidity, and air pressure. This research uses a quantitative approach with the MLP model. BMKG data is processed through preprocessing stages, which include monthly feature formation, normalisation, and cleaning. The model is trained with the backpropagation algorithm to optimise the climate pattern classification process. The research results show that MLP is capable of capturing seasonal variations, especially during the transition periods, and can classify monthly climate patterns with high accuracy. Model evaluation indicates stable performance based on accuracy, precision, recall, and F1 score metrics. For classifying monthly climate patterns in North Sumatra, MLP is effectively used. This can also serve as an alternative to the BMKG historical data-based prediction methods that are more suited to nonlinear climate patterns.
Performa Multinomial Naive Bayes dalam Klasifikasi Misinformasi Megathrust pada Komentar TikTok Febri Yalda Sulistia; Karina Nurfebia; Nurbeti Sinulingga; Nuzul Aini Ramadhani; Khairul Khairul
Indonesian Journal of Education And Computer Science Vol. 4 No. 2 (2026): INDOTECH - August 2026
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v4i2.2090

Abstract

Penyebaran misinformasi terkait bencana megathrust di media sosial TikTok berpotensi memicu kepanikan publik dan mengganggu efektivitas komunikasi risiko bencana. Penelitian ini bertujuan menganalisis model klasifikasi informasi valid dan misinformasi terkait megathrust menggunakan pendekatan Natural Language Processing (NLP), algoritma Multinomial Naive Bayes, dan TF-IDF. Data penelitian berupa 2.000 komentar TikTok berbahasa Indonesia yang dikumpulkan secara purposive sampling dari konten relevan dan dibagi ke dalam dua kelas: informasi valid dan misinformasi. Preprocessing meliputi case folding, cleaning, tokenizing, stopword removal, dan stemming menggunakan library Sastrawi. Evaluasi pada 400 data uji menghasilkan akurasi 52%, macro-precision 50,24%, macro-recall 50,20%, dan macro-F1 48,93%. Akurasi tersebut lebih rendah daripada majority-class baseline 54,5%, sehingga model belum memberikan peningkatan dibanding prediksi sederhana yang selalu memilih kelas mayoritas. Kesalahan paling banyak terjadi pada komentar valid yang diprediksi sebagai misinformasi. Hasil ini menunjukkan bahwa variasi bahasa informal dan keterbatasan TF-IDF dalam menangkap konteks masih menjadi tantangan utama pada klasifikasi komentar TikTok
Co-Authors . Zulfan Abdur Rahman Ade Iskandar Adek Maulidya Agus Junaidi Ahyanuardi Ahyanuardi Albertus Tua Simanullang Alex Siregar Ali Ikhwan Amin, Muhammad Ananda Aulia Andi Ernawati Andi Ernawati Andysah Putera Utama Siahaan Anzas Ibezato Zalukhu Aradi Sebayang Ardya, Dwika Ari Anshari Yuliansyah Siregar Arip Muhridan Asrul Helmandi Asyahri Hadi Nasyuha Ayu Husniyyah Ayu Ofta Sari Barus, Efriansyah Putra Bahari Beni Satria Boy Rizki Akbar Chelfina Utami Darmeli Nasution Debby Keumala Sari Dedy Chandra Wardani Dedy Rahman Harahap Dicky Lesmana Dina Marsauli Sibarani Dwi Faza Wardanu Damanik Edy Sarwono Ponco Eko Budianto Eko Hariyanto Eko Wahyudi Fachrina Wibowo Febri Yalda Sulistia Feby Wulandari Sembiring Fery Anugerah Glorynta S B Nadeak Glorynta S B Nadeak Helmy, Ahmad Hermansyah Hindra Syahputra Hrp, Abdul Khaidir Ibnu Afandi Manao Ibnu Rasyid Munthe Ihsan Fahreza Indra Wadiasto Irfan Abadi Saragih Irfan Nainggolan Irwan Syahputra Iwan Purnama Jodi Hendrawan Karina Nurfebia Khairil Putra Krisna Diva Laila Maghfirah Langgeng Restuono Leni Marlina M. Azwan Mhd Ihsan Abidi Moustafa H. Aly Muhammad Azuan Muhammad Erpandi Dalimunthe Muhammad Fahriza Muhammad Fuad Hafiz Muhammad Hasanuddin, Muhammad Muhammad Iqbal Muhammad Irfan Sarif Muhammad Zainal Arifin Pohan Natalia Hernawaty Nahampun Nicky Syahputra Niska, Debi Yandra Nurbeti Sinulingga Nuzul Aini Ramadhani Parisca Parisca Putri Khairunnisa Rahmad Budi Utomo Rahmaniar Rahmaniar Rahmat Rezki Rahmat Rezki Reza Fahromi Reza Fahromi Rian Farta Wijaya Rio Septian Hardinata Risnawati Agustin Rizaldi, Fakhri Rizky Rinaldi Ronal Watrianthos Ruth Rize Paas Megahati.S Samuel Sampe Tuah Purba Septia Harliansyah Simamora, Siska Siti Nurhaliza Sofyan Sitorus, Zulham Sri Haryati Sri Nadriati Sriwahyuni Suhardiansyah Suherman Suherman Sukrianto Sumiran Sumiran Syahri T., Siti Isna Tegar Nabilleon Mandiri Siregar Tengku Didi Ferdillah Toni Prabowo Tri Cahya Agung Virdyra Tasril Willi Eldipan Simatupang Wina Aulia Yanti Yusman Zulfahmi Syahputra Zulham Sitorus Zulkifli