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All Journal Indonesian Journal of Applied Informatics Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Teknik Informatika dan Teknologi Informasi
Imam Budiawan
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Other

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Analisa Prediksi Kelulusan Mahasiswa Menggunakan Metode Machine Learning: Penelitian Abdul Khalik Walya; Hasbi Rizki Sulistyo; Ibnu Agustian Pratama; Sifatul Akmal; Imam Budiawan; 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.v4i3.4959

Abstract

Student graduation prediction is an important issue in higher education as it is closely related to the evaluation of academic success. Various machine learning algorithms have been applied to predict student graduation based on academic data. This study conducts a comparative analysis of three classification algorithms, namely Logistic Regression, Random Forest, and K-Nearest Neighbor, using a simulated dataset consisting of 200 student records with attributes including age, department, GPA, and graduation year. The research stages include data preprocessing, data splitting, model training, and performance evaluation using classification metrics. Experimental results indicate that Logistic Regression and Random Forest achieve the best performance with an accuracy of 100%, while the K-Nearest Neighbor algorithm attains an accuracy of 80%. These findings highlight that data characteristics and algorithm selection significantly affect the accuracy of student graduation prediction.
CLAHE-Enhanced YOLOv8: Deteksi Pelanggaran Helm Real-Time pada Citra CCTV Low-Light: Penelitian Devin Nurman Wijaya; Dedy Ariyanto; Prasetyo Bintang S.N; Levina Cecilia P; Imam Budiawan; 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.v4i3.4964

Abstract

Low lighting conditions in CCTV images cause a decrease in the accuracy of the Electronic Traffic Law Enforcement (E-TLE) system, especially in detecting helmet use among motorcyclists. Dark images with low contrast and high noise hinder the feature extraction process, so that deep learning-based detection models often produce False Negatives. This study proposes the integration of the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing method with the YOLOv8 architecture to improve the performance of helmet violation detection in low-light environments. The Helmet Detection dataset is used with the addition of synthetic low-light augmentation to simulate variations in nighttime lighting intensity. Tests show that the use of CLAHE can significantly improve the quality of visual features, as evidenced by the increase in Mean Average Precision (mAP@0.5) from 72.4% in raw images to 89.1% after preprocessing. In addition, the system is still able to operate in real-time with an average speed of 35–37 FPS on a Tesla T4 GPU. These results indicate that the integration of CLAHE and YOLOv8 is effective in improving the reliability of helmet violation detection in low-light conditions and is feasible to be implemented in computer vision-based traffic surveillance systems.
Analisa Komparasi Kinerja Algoritma K-Nearest Neighbor (K-NN) dan Decision Tree dalam Klasifikasi Situs Web Phising: Penelitian Fajar Dwi Prasetyo; Muhammad Maulana; Faris Ramadhan; Ananda Lutfi Setiabudi; Imam Budiawan; 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; Imam Budiawan; Feri Andrianto; Reafael Andrian Canavaro; Muhammad Haikal Nugroho; Sofiyan Aris Saputra
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 Java Diovanka Alam; Musyaffa Ramdhan; Muhammad Yuzakki Raja Rafael; Muhammad Faiz Hamka; Desmulyati Desmulyati; Imam Budiawan
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 Ade Ikhsanudin Setiawan Wardhana; Galih Min Fadlil; Raihan Putra Wirahman; Deny Wahyu Fahrani; Imam Budiawan; 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 Khalisa Salsabila; Nahya Faulya Maulidia; Shabrina Auliya Zahra Hafid; Aisyah Shinta Balqis; Imam Budiawan; 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 Avrillistianto Ananda Nabil; Farih Ramdan Wildantama; Dimas Satrianto; Michael Gilbert Bakara; Imam Budiawan; Desi Mulyati
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 Adhimas Prakoso; Sandra Bagus Nugroho; Naufal Aqiil Nugraha; Fendi Ferdiansyah; Imam Budiawan; 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.
Pemodelan Prediktif Emisi CO2 Kendaraan Kanada: Studi Komparatif Neural Network dan Support Vector Machine Rifki Nur Hidayat Putra; Nindya Dwi Lestari; Dinda Aprillia; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
IJAI (Indonesian Journal of Applied Informatics) Vol 10, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v10i1.110736

Abstract

Abstrak : Sektor transportasi merupakan penyumbang emisi karbon dioksida (CO2) terbesar yang memperparah perubahan iklim. Penelitian ini bertujuan mengembangkan model prediktif yang akurat untuk memperkirakan emisi CO2 kendaraan dengan memanfaatkan pendekatan pembelajaran mesin. Dataset yang digunakan adalah data emisi kendaraan Kanada dari Kaggle. Metode yang diterapkan adalah Support Vector Machine (SVM) dan Neural Network untuk menganalisis pola kompleks dari berbagai parameter teknis kendaraan, seperti ukuran mesin, jumlah silinder, dan jenis transmisi. Hasil penelitian menunjukkan bahwa Neural Network secara konsisten unggul dibandingkan SVM dengan tingkat akurasi prediksi melebihi 90% dan nilai F1-score mencapai 0,831 untuk model SVM serta 0,954 untuk model Neural Network, yang menunjukkan kinerja klasifikasi yang kuat dan konsisten. Neural Network juga terbukti lebih baik dalam menangkap hubungan non-linier antara karakteristik kendaraan dan emisi CO2. Keberhasilan model ini membuka peluang pengembangan model prediktif yang lebih canggih serta dapat menjadi dasar bagi pembuat kebijakan dalam merancang regulasi emisi yang lebih akurat dan berbasis data.=====================================================Abstract :The transportation sector is the largest contributor to carbon dioxide (CO2) emissions that exacerbate climate change. This research aims to develop an accurate predictive model to estimate vehicle CO2 emissions by utilizing a machine learning approach. The dataset used is Canadian vehicle emissions data from Kaggle. The methods applied are Support Vector Machine (SVM) and Neural Network to analyze complex patterns of various vehicle technical parameters, such as engine size, number of cylinders, and transmission type. The results showed that the Neural Network consistently excelled over SVM with a prediction accuracy rate exceeding 90% and an F1-score value of 0.831 for the SVM model and 0.954 for the Neural Network model, indicating a strong and consistent classification performance. Neural networks have also been shown to be better at capturing the non-linear relationship between vehicle characteristics and CO2 emissions. The success of this model opens up opportunities for the development of more sophisticated predictive models and can serve as a basis for policymakers to design more accurate and data-driven emissions regulations.
Co-Authors Abdul Khalik Walya Ade Ikhsanudin Setiawan Wardhana Adhimas Prakoso Aisyah Shinta Balqis Alghifar Firgiawan Alwan Kapi Muntaha Alya Avisa Amar Khadafi Ananda Lutfi Setiabudi Andika Amansyah Arnata Nur Rasyid Aryo Satrio W Asmawati Asmawati Audy Aulia Azzahra Avrillistianto Ananda Nabil Cahyani Ayu Sulistyawati Dedy Ariyanto Deny Wahyu Fahrani Desi Mulyati Desiana Nuranudin Putri Desmulyanti Desmulyanti Desmulyati, Desmulyati Devin Nurman Wijaya Dimas Satrianto Dinda Aprillia Dzattho Key Fani Erlangga Rizki Ekaptra Fajar Dwi Prasetyo Fajar Yoga Adiansyah Farhan Fadhilah Farih Ramdan Wildantama Faris Ramadhan Faris Syahrendra Fauzan Nawwir Andriansyah Fendi Ferdiansyah Feri Andrianto Galih Min Fadlil Ginting Wibi Prasetyo Hasbi Rizki Sulistyo Ibnu Agustian Pratama Imam Wahyudi Iqro Mukti Arto Java Diovanka Alam Joseph Melchior Nababan Kevin Dwi Satria Khalisa Salsabila Levina Cecilia P M. Iqbal GF Michael Gilbert Bakara Muhammad Faiz Hamka Muhammad Haikal Nugroho Muhammad Maulana Muhammad Raviansyah Muhammad Yuzakki Raja Rafael Musyaffa Ramdhan Nahya Faulya Maulidia Naufal Aqiil Nugraha Nindya Dwi Lestari Paulus Paulus Prasetyo Adi Suwignyo Prasetyo Bintang S.N Putra Satria Raihan Naufal Ramadhan Raihan Putra Wirahman Reafael Andrian Canavaro Rifki Nur Hidayat Putra Roida Pakpahan Roni Saputra Pratama Ryehan Alfiansyah Sandra Bagus Nugroho Shabrina Auliya Zahra Hafid Sifatul Akmal Sofiyan Aris Saputra Sumanto Sumanto Tarmidzi Ibrahim Vemi Januar Pratama Widya Viona Septi Tanjung Zahwa Asfa Rabbani