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All Journal Jurnal CoreIT JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL PENDIDIKAN TAMBUSAI IJISTECH (International Journal Of Information System & Technology) EQIEN - JURNAL EKONOMI DAN BISNIS Jurnal Infortech IJISTECH Jurnal Media Informatika Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Abdimas Dosma (JAD) Jurnal Ilmiah Teknik Informatika dan Komunikasi Socius: Social Sciences Research Journal Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Ilmiah Sistem Informasi Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal Abdimas Dosma
Imam Budiawan
Universitas Nusa Mandiri
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Analisa Komparasi Kinerja Algoritma K-Nearest Neighbor (K-NN) dan Decision Tree dalam Klasifikasi Situs Web Phising: Penelitian Prasetyo, Fajar Dwi; Maulana, Muhammad; Ramadhan, Faris; Setiabudi, Ananda Lutfi; Budiawan, Imam; Desmulyati, Desmulyati
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4965

Abstract

Phishing attacks represent a significant cybersecurity threat aimed at stealing sensitive user information through psychological manipulation using fake websites. Conventional detection methods relying on blacklists are considered ineffective in recognizing zero-day attacks or newly published phishing sites. This study aims to develop an automated detection model using a Machine Learning approach by comparing the performance of two Supervised Learning algorithms: K-Nearest Neighbor (K-NN) and Decision Tree. The dataset used is sourced from the UCI Machine Learning Repository, consisting of 11,055 records with 30 URL characteristic features. Performance evaluation was conducted using Accuracy metrics and Confusion Matrix analysis. Experimental results indicate that the Decision Tree algorithm significantly outperforms K-NN with an accuracy of 95.21%, while K-NN achieved an accuracy of only 60.11%. Furthermore, Decision Tree demonstrated a very low False Negative rate, making it a more recommended model for real-time cybersecurity system implementation.
Analisis Pengelompokan Pola Pembayaran UKT Mahasiswa Menggunakan Algoritma K-Means Clustering: Penelitian Desmulyati, Desmulyati; Budiawan, Imam; Andrianto, Feri; Canavaro, Reafael Andrian; Nugroho, Muhammad Haikal; Saputra, Sofiyan Aris
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4967

Abstract

Single Tuition Fee (UKT) plays a crucial role in financing higher education, but late and arrear payments are often difficult to analyze manually. This study aims to classify student UKT payment patterns using the K-Means algorithm based on per capita income, UKT amount, lateness, lateness category, and total arrears. The data used were 300 cleaned and standardized students. The number of clusters was determined using the Elbow and Silhouette Score methods, with the best results at k = 3 (SSE = 524.06; Silhouette Score = 0.5609). The three clusters include high-income students with regular payments, low-income students with minor delays, and high-risk students with large delays and arrears. These results help universities map UKT payment risks and develop more targeted collection and relief policies.
Analisis Kepuasan Pelanggan terhadap Beberapa Produk yang di Jual di E-Commerce Menggunakan Metode Naïve Bayes dan Logistic Regression: Penelitian Alam, Java Diovanka; Ramdhan, Musyaffa; Rafael, Muhammad Yuzakki Raja; Hamka, Muhammad Faiz; Desmulyati, Desmulyati; Budiawan, Imam
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4970

Abstract

Customer satisfaction is a crucial element that plays a significant role in the sustainability of businesses in the e-commerce sector. Reviews provided by consumers serve as an important source of information to assess how satisfied they are with the products they purchased. This study aims to evaluate customer satisfaction levels using product review data through two classification methods: Multinomial Naive Bayes and Logistic Regression. The data used comes from a real Indonesian-language dataset that includes review texts and buyer ratings. The research process consists of several stages, starting from text preprocessing, feature extraction using the TF-IDF method, satisfaction label grouping, model training, and evaluation using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The findings of this study indicate that both methods can predict customer satisfaction with competitive accuracy. Logistic Regression demonstrates more consistent results compared to Naive Bayes in the context of Indonesian-language text. These results can be utilized by e-commerce companies to monitor product quality and continuously improve services for consumers.
Perbandingan Model Machine Learning dalam Prediksi Penyakit Jantung dengan Optimalisasi Fitur Gejala dan Faktor Risiko: Penelitian Wardhana, Ade Ikhsanudin Setiawan; Fadlil, Galih Min; Wirahman, Raihan Putra; Fahrani, Deny Wahyu; Budiawan, Imam; Desmulyati, Desmulyati
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4972

Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early detection of its risk crucial to prevent severe complications. This study develops a heart disease risk prediction system using machine learning techniques, including Random Forest, Logistic Regression, and Support Vector Machine (SVM). The dataset is processed through several stages, including numerical feature selection, feature engineering with the addition of a total symptoms variable, and class imbalance handling using class-weight adjustments The model training process involves splitting the data into training and testing sets, followed by evaluation using accuracy, confusion matrix, and classification report metrics. The system also integrates an interactive interface that allows users to select symptoms and risk factors through widget-based checklists, enabling real-time prediction. The results show that the best-performing model achieves high accuracy and effectively identifies the most influential factors based on feature importance analysis. These findings indicate that machine learning provides a reliable and efficient tool to support early risk detection of heart disease.
Analisis Prediksi Nilai Akhir Mahasiswa Menggunakan Algoritma Regresi Linear Berbasis Machine Learning pada Program Studi Teknologi Informasi Universitas Bina Sarana Informatika: Penelitian Salsabila, Khalisa; Maulidia, Nahya Faulya; Hafid, Shabrina Auliya Zahra; Balqis, Aisyah Shinta; Budiawan, Imam; Desmulyati, Desmulyati
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4975

Abstract

The development of information technology in education demands a fast, objective, and data-driven academic evaluation system. Problems in higher education often involve lecturers' difficulty in monitoring and predicting student academic performance early, resulting in delayed response to declining performance. One solution that can be implemented is the use of Machine Learning. This study aims to analyze the prediction of students' final grades using a Machine Learning-based Linear Regression algorithm with attendance and assignment grades as variables. The case study was conducted on students of the Information Technology Study Program at Bina Sarana Informatika University using simulated data of 100 students, with the data divided into 80% training and 20% testing. Model evaluation used MSE, RMSE, and R². The results showed an R² value of 0.94, which means that 94% of the variation in students' final grades can be explained by attendance and assignment grades, while 6% is influenced by other factors. These findings indicate that the Linear Regression algorithm has excellent predictive performance in predicting students' final grades objectively and data-driven.
Klasifikasi Hoax Menggunakan Metode TF-IDF + SVM: Penelitian Nabil, Avrillistianto Ananda; Wildantama, Farih Ramdan; Satrianto, Dimas; Bakara, Michael Gilbert; Budiawan, Imam; Mulyati, Desi
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5078

Abstract

The spread of hoax news on social media causes social unrest and economic losses. This study builds a classification model for Indonesian hoax news using Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM). The dataset consists of 970 news from TurnBackHoax.id with FALSE and FRAUD categories. The research includes text preprocessing, TF-IDF feature extraction with unigram and bigram, and linear kernel SVM classification. Data was split 80:20 using stratified sampling with parameter optimization through Grid Search and 5-fold Cross Validation. Evaluation results show the model classifies hoax news with good performance based on accuracy, precision, recall, and f1-score metrics. The confusion matrix indicates most data was correctly classified despite errors in news with overlapping linguistic patterns. The study proves TF-IDF and SVM combination is effective for Indonesian hoax detection with low computational requirements. Further development is recommended using larger datasets and comparing with deep learning methods.
Prediksi Churn Pelanggan Telekomunikasi Menggunakan Metode Supervised Learning dengan Random Forest dan XGBoost: Penelitian Prakoso, Adhimas; Nugroho, Sandra Bagus; Nugraha, Naufal Aqiil; Ferdiansyah, Fendi; Budiawan, Imam; Desmulyanti, Desmulyanti
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5079

Abstract

Customer churn is a major challenge in the telecommunications industry, resulting in revenue losses. Therefore, the ability to predict customers at risk of churn is crucial for preventative measures. This study developed and compared ensemble-based churn prediction models, namely Random Forest and XGBoost, using historical customer data covering demographics, service, and usage aspects, through pre-processing, training, and model evaluation stages. The results show that both models perform well, but XGBoost excels in AUC and F1-Score metrics, indicating better discriminatory ability and precision-recall balance. Feature importance analysis identified key churn factors, such as Monthly Charges and Tenure, which provide a basis for companies to design more focused and effective retention strategies.
Co-Authors Achmad Rivai Syahputra Ade Christian, Ade Ade Setiawan Ahmad Yusuf Akmal, Sifatul Alam, Java Diovanka Amas Sari Marthanti Antony Pangaribuan, Rizky Daud Arfhan Prasetyo - STMIK Nusa Mandiri Arfhan Prasetyo - UBSI ARIYANTO, DEDY Astriana Mulyani Astriana Mulyani, Mulyani Bakara, Michael Gilbert Balqis, Aisyah Shinta Bella - STMIK Nusa Mandiri Bella - UBSI Belsana Butar Butar Bintang S.N, Prasetyo Canavaro, Reafael Andrian Cecilia P, Levina Desi Mulyati Desmulyanti, Desmulyanti Desmulyati, Desmulyati Dwi Arum Ningtyas Faatin, Safinah Fadlil, Galih Min Fahrani, Deny Wahyu FAHRIZAL Fani, Dzattho Key Fathur Rismansyah Ferdiansyah, Fendi Feri Andrianto Firstianty Wahyuhening Fibriany Gata, Windu GF, M. Iqbal Hafid, Shabrina Auliya Zahra Hamka, Muhammad Faiz Hani Harafani Hani Harafani, Hani Hari, Raga Suhada Henny Leidiyana Hoiriah Hoiriah I Ispandi Ika Kurniawati Imam Sujarwo ispandi ispandi Ispandi Ispandi Ispandi Ispandi, Ispandi Karo-Karo, Julkarnaen Khadafi, Amar Kiswanto, Andi Diah Martua HamiSiregar Maulidia, Nahya Faulya Muhammad Maulana Muhammad Nasrulloh Mulyadi Mulyadi Mulyani, Astriana Musfiroh Musfiroh, Musfiroh Mushliha Nabil, Avrillistianto Ananda Nugraha, Naufal Aqiil Nugroho, Muhammad Haikal Nugroho, Sandra Bagus Nurrahman, Alvin Pakpahan, Roida Prakoso, Adhimas Prasetya, Arfhan Prasetyo, Fajar Dwi Pratama, Ibnu Agustian Primadana, Raihan Putra, Imam Hanif Rafael, Muhammad Yuzakki Raja Raihan Raihan, Raihan Ramadhan, Faris Ramdhan, Musyaffa Rian Hidayat Rizky, Fernando Jovanca Roida Pakpahan Romdonni, Achmad Roddi Rusli, Ahmad Rais Salsabila, Khalisa Saputra, Sabita Abigail Saputra, Sofiyan Aris Satrianto, Dimas Satrio W, Aryo Sefriani, Shintia Putriayu Setiabudi, Ananda Lutfi Sidik Sidik Sidik Sidik Sidik Suci, Bintang Dyas Sulistyo, Hasbi Rizki Sulistyowati, Daning Nur Sumanto, Sumanto Sungkar, Adam Andrea Susan Rachmawati Syakir, Adryan Raihan Umar, Muhammad Hussein Ummu Radiyah Ummu Radiyah Ummu Radiyah, Ummu Walya, Abdul Khalik Wardhana, Ade Ikhsanudin Setiawan Wati Erawati Wijaya, Devin Nurman Wildantama, Farih Ramdan Wirahman, Raihan Putra Yasin, Saeful Yunardus, Yunardus Yuris Alkhalifi Zalmi, Indah Oktavia