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All Journal Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Pendidikan Teknologi dan Kejuruan Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) PROCEEDING IC-ITECHS 2014 SMATIKA E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Technomedia Journal KOMPUTIKA - Jurnal Sistem Komputer Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Tekno Kompak Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Teknologi Pendidikan : Jurnal Penelitian dan Pengembangan Pembelajaran Bulletin of Computer Science Research Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer AKM: Aksi Kepada Masyarakat Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Jurnal Algoritma Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian Masyarakat Bangsa Bulletin of Informatics and Data Science Jurnal Ilmiah Computer Science Journal of Information Technology, Software Engineering and Computer Science Management of Information System Journal JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Smatika Jurnal : STIKI Informatika Jurnal Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat Jurnal Komputasi
Heni Sulistiani
Universitas Teknokrat Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Klasifikasi Tingkat Risiko Gempa di Indonesia Menggunakan Pola Spasial dan Temporal Berbasis Decision Tree Mugi Prasetio; Heni Sulistiani; Onassis Yusuf Inonu; Kardita Magda; Budi Santosa
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.624

Abstract

Indonesia is an area that is very vulnerable to earthquakes due to its location in the meeting zone of active tectonic plates. This study aims to classify the level of earthquake risk based on spatial and temporal patterns using the Decision Tree method as a solution in predicting potential earthquake hazards. The data used is earthquake data in Indonesia from 2015 to 2023 obtained from public datasets, including location information (latitude and longitude), event time (year and month), and earthquake magnitude. Earthquakes are categorized into three risk classes: Low (M < 4.0), Medium (4.0 ? M < 6.0), and High (M ? 6.0). The Decision Tree model was successfully built with an average accuracy of 88% on the test data. The results show that earthquakes mostly occur in active subduction zones such as the Sunda Subduction Zone (Sumatra and Java), Banda Arc (Nusa Tenggara, Maluku, Seram), Sulawesi, and Papua. Temporal analysis also shows fluctuations in the number of earthquakes by year and season, with increased activity in certain months. The spatial visualization reinforces the finding that the eastern region of Indonesia is more seismically active than the western region. This research proves that machine learning approaches can be used to support earthquake disaster mitigation through historical data-based risk identification.
Analisis Perbandingan Kinerja Algoritma Machine Learning Untuk Classifikasi Kesehatan Mental Mahasiswa Muhammad Chanafy; Heni Sulistiani
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2080

Abstract

Mental health issues among college students are a critical issue that requires data-driven approaches to detect early treatment needs. This study aims to analyze and compare the performance of three machine learning algorithms: Naive Bayes, K-Nearest Neighbor (K-NN), and Decision Tree in classifying college students' mental health treatment needs based on an open survey dataset. The study was conducted systematically using RapidMiner software, with data preprocessing, model training, testing, and performance evaluation using accuracy, precision, and recall metrics. The test results showed that the Naive Bayes algorithm produced an accuracy of 78.85%, a precision of 75.96%, and a recall of 72.84%. K-NN performed better with an accuracy of 82.62%, a precision of 80.83%, and a recall of 77.37%. Meanwhile, the Decision Tree algorithm performed best with an accuracy of 88.32%, a precision of 86.77%, and a recall of 85.80%. In addition to its high performance, Decision Tree also offers advantages in interpreting results through its decision tree structure, which illustrates the role of variables such as employment status (self_employed), family history (family_history), survey completion time (timestamp), and care options (care_options) in the classification process. Decision Tree can be concluded as the most effective classification model for detecting student mental health needs in this data context. These findings are expected to serve as a reference in the development of machine learning-based early detection systems to support mental health policies and interventions in higher education settings.
Klasifikasi Kesehatan Mental Menggunakan Support Vector Machine Berdasarkan Screen Time dan Interaksi Sosial Digital Pendi Pendi; Heni Sulistiani
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9189

Abstract

Mental health is an important aspect that influences the quality of life of individuals, especially in adolescents and young adults who are vulnerable to stress due to the increased use of digital devices. Technological developments have led to increased screen time and the intensity of digital social interactions, which have the potential to affect mentsal health conditions. This study aims to develop a mental health classification model using the Support Vector Machine (SVM) method with a Radial Basis Function (RBF) kernel based on digital behavior data, including daily device usage time, social media time, number of positive interactions, and number of negative interactions. The dataset used is secondary data obtained from Kaggle and goes through the stages of pre-processing, feature selection, data normalization, and division of training and test data with a ratio of 80:20. The built SVM model is able to classify mental health conditions into three classes, namely Healthy, Stressed, and Risky. The evaluation results show that the accuracy of the resulting model is 94.3%, with a precision value of 66.3%, a recall of 96.1%, and an f1-score of 74.1%. These results indicate that the variables of screen time and digital social interaction have strong potential to be used as a basis for objective and data-based mental health classification.
Machine Learning Comparative Analysis of SVR Method with RBF Kernel and Random Forest for Bitcoin Price Prediction Miko Septa Pratama; Heni Sulistiani
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9348

Abstract

This study aims to determine how accurate machine learning predictions are for predicting Bitcoin prices using the SVR With RBF Kernel and Random Forest methods. This study was conducted because Bitcoin’s volatility is so high that it is difficult to predict. Therefore, this study uses two different methods to allow for a more objective evaluation of model characteristics on volatile data. The dataset was obtained through Kaggle with a Bitcoin price dataset from 2018 to October 2025, totaling 2,856 datasets in CSV format. After training both methods on the same dataset, price prediction results were obtained. Support Vector Regression (SVR) With RBF Kernel achieved a relatively high data evaluation result with an MAE of 10866.882878735294, MSE of 204836847.5591309, and RMSE of 14312.12239883138, while the Random Forest method achieved a low data evaluation result with an MAE of 19342.47, MSE of 659671833.13, and RMSE of 25684.08. The result of these two methods show a significant difference, with Random Forest more closely aligning with the acual data, with a lower evaluation value and producing values closer to the actual data. This research was conducted to determine the accuracy of the Support Vector Regression (SVR) with RBF Kernel and Random Forest algorithms. It is concluded that both methods make good predictions, only the Random Forest method is closer to the actual Bitcoin price.
Perbandingan Linear Regression dan Random Forest untuk Prediksi Harga Cryptocurrency Hadid Abdilla; Heni Sulistiani
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

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

Abstract

Perkembangan Cryptocurrency yang pesat menjadikan aset digital seperti BNB dan ETH semakin diminati oleh investor, namun volatilitas harga yang tinggi juga meningkatkan risiko kerugian. Oleh karena itu, diperlukan metode prediksi yang dapat membantu memahami arah pergerakan harga secara lebih terukur. Penelitian ini bertujuan untuk memperkirakan pergerakan harga aset Cryptocurrency secara berbasis data. Penelitian ini juga bertujuan untuk mengukur kemampuan model dalam menangkap pola harga pada aset yang bersifat volatil menggunakan pendekatan Machine Learning, yaitu Random Forest Regression dan Linear Regression. Data yang digunakan berupa data historis periode 2020-2025. Proses pengolahan data meliputi preprocessing data, penerapan model Machine Learning dan evaluasi kinerja model menggunakan MSE, R², dan MAPE. Hasil penelitian menunjukkan bahwa model Random Forest Regression dan Linear Regression memiliki performa yang berbeda dalam memprediksi harga Cryptocurrency BNB dan ETH. Linear Regression menghasilkan MAPE 5,14% pada BNB dan 8,17% pada ETH. Sementara itu, Random Forest Regression menunjukkan performa yang lebih rendah dengan MAPE 15,02% pada BNB dan 9,65% pada ETH. Nilai tersebut menunjukkan tingkat kesalahan prediksi yang bervariasi. Dengan demikian, Linear Regression dinilai lebih mampu menangkap pola data yang kompleks dan fluktuatif, sehingga lebih direkomendasikan sebagai model prediksi harga Cryptocurrency dalam penelitian ini. Kata kunci : Random Forest Regression; Linear Regression; Cryptocurrency; BNB; ETH; Machine Learning
Optimasi Hyperparameter Grid Search untuk Multi-Algoritma Klasifikasi Tingkat Hidrasi pada Daily Water Intake Julfiana Rizkiah Futri; Heni Sulistiani
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10560

Abstract

Water is a vital element for sustaining human physiological functions, ranging from metabolic processes to cognitive and physical performance. Insufficient daily water intake can trigger various health problems that significantly reduce individual well being. Machine learning provides a data driven approach to classify hydration levels more objectively based on individual and environmental attributes. This study evaluates and compares the performance of three classification algorithms Random Forest, Naive Bayes, and K-Nearest Neighbor in predicting hydration status (Good or Poor) using the Daily Water Intake public dataset comprising 30,000 samples. Class imbalance in the dataset (79.7% Good, 20.3% Poor) was addressed through SMOTE, while Stratified K-Fold Cross Validation with K=10 was employed for model evaluation. Hyperparameter optimization was performed using Grid Search, and model performance was assessed through accuracy, precision, recall, F1-Score, and AUC-ROC metrics. Results demonstrate that Random Forest achieved the highest accuracy of 99.50% and AUC-ROC of 0.9999 after optimization thanks to its ensemble mechanism, which captures non-linear relationships among features, outperforming Naive Bayes, which is constrained by its feature-independence assumption. KNN showed the most notable improvement post optimization with an accuracy gain of 0.62% (from 97.62% to 98.24%), while Naive Bayes remained unchanged at 83.53% as its optimal parameter matched the default value. These findings offer evidence based guidance for algorithm selection in hydration classification systems.
Implementasi Teknik SMOTE Menggunakan Random Forest dan XGBoost pada Klasifikasi Tingkat Kualitas Udara Nanda Putri Karizki; Heni Sulistiani
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10602

Abstract

Air pollution is a global environmental issue with significant impacts on public health and environmental sustainability. The problem is compounded by the generally imbalanced class distribution in air quality data; consequently, the highest-risk category "Hazardous" often constitutes the minority class, making it the most difficult to detect using conventional classification models. This study implements a machine learning approach using Random Forest and XGBoost algorithms combined with the Synthetic Minority Oversampling Technique (SMOTE) to classify air quality levels. The dataset comprises 5,000 samples featuring nine environmental and demographic variables. Air quality is categorized into four classes Good, Moderate, Poor, and Hazardous with an initial imbalanced distribution (Good: 40%, Moderate: 30%, Poor: 20%, Hazardous: 10%). SMOTE was applied exclusively to the training data to balance the class distribution. Results indicate that XGBoost combined with SMOTE achieved the best performance, yielding an accuracy of 0.952, an F1-Score of 0.952, and an average cross-validation score of 0.969. This represents a 0.022 improvement over a previous study that utilized a Decision Tree model without SMOTE (achieving 0.930 accuracy). For the "Hazardous" minority class, the XGBoost-SMOTE combination improved recall from 0.80 (Random Forest without SMOTE) to 0.87, while also achieving more balanced precision and F1-Score values ​​(0.87). CO levels and proximity to industrial areas emerged as the dominant features, contrasting with PM2.5 in the earlier study. These findings confirm that combining ensemble methods with SMOTE effectively addresses class imbalance. Evaluation was conducted using precision, recall, and F1-Score metrics, alongside 5-fold stratified cross-validation to ensure model stability across all classes including the "Hazardous" minority class, which is most critical for public health. These results suggest that combining ensemble algorithms with data balancing techniques can serve as a practical reference for developing machine learning-based air quality monitoring systems.
Co-Authors Ade Dwi Putra Ade Dwi Putra Adelia Pratiwi Admi Syarif Ady Chandra Agung Pria Laksono Agung Saputra Agus Irawan Agus Irawan Agustina, Intan Ahmad Ari Aldino Ahmad Fawaiq Suwanan Ahmad Januar Amriyansah Aidil Akbar Akbar, Muhammad Fadil Alfarizi, Ferdian Alfikri, Valbian Alif Danang Pinangkis Alita, Debby Altarik Aziz Alvi Suhartanto Alvi Suhartanto Alvi Suhartanto Alvinan Virgilia Andi Nurkholis Andika, Rio Andre, Muhammad Fabio Ani Sesanti Antoni, Kevin Rizki Anwar, Adi Khairul Anwar, Rian Aprian Nuriansah Ari Sulistiyawati Arief Aryudi Syidik Arif Munandar Arshad, Muhammad Waqas Arsi Hajizah Auliya R. Isnain Bagastian Bagastian Bagus Dwi Prasetyo Bagus Miftaq Hurohman Bambang Dwi Setyarto Benhouzer N.P Pasaribu BP Putra Hermana Budi Santosa Cici Dian Paramita Damayanti Damayanti Damayanti Damayanti Damayanti, Damayanti Darwanto, Imam Dedi Darwis Dedi Darwis Dewantoro, Fajar Dimas Eko Putro Dimas, Novario Dirgantara Mardha Dilansyah Doris Juarsa Eka Lisna Rahmadani Eko Bagus Fahrizqi Elin Gusbriana Elvano Delisa Mega Erliyan Redy Susanto Esy Ervina Yanti Evi Dwi Wahyuni Fahreza Aditya Aryatama Falssava, Jossa Neka Fatmawati Isnaini Fatriana, Nina Fikri Hamidy Gaib Wiwaha, Gigant Geri Marizki Greessheilla Phylosta P.B Gunawan, Rakhmat Dedi Hadid Abdilla Hamdan Sobirin, Muhammad Hati, Clifansi Remi Siwi hendri eka pratama Hendrik Saputra Heru Setiawan I Gede Heri Susanto Icha Winadya Permadani Ikbal Yasin Ikbal Yasin Ilham Muhammad Ghoffar Imam Ahmad Imam Ahmad Ismail, Izudin Ismail, Izzudin Isnain, Auliya Rahman Istiana, Winda Iwan Purwanto Izka, Ade Adyatna Izudin Ismail Jefri Jaka Tirta Julfiana Rizkiah Futri Junaidi Junaidi Kardita Magda Khairun Nisa Khoirunnisa, Yosi Koswara, Wawan Kurnia Muludi M. Sholahuddin Al-Ayyubi Maheswari, Diva Afirlia Masnia Rahayu Maulida Waya Inayah Mauludi, Ilham Moenir Megawaty, Dyah Ayu Mehta, Abhishek Meutia Kartika Arisandi Miko Septa Pratama Miswanto Miswanto Mugi Prasetio Muhammad Chanafy Muhammad Fahmi Fudholi Muhammad Hamdan Sobirin Muhammad Syahril Muhaqiqin muhaqiqin Nanda Putri Karizki naufal, wandi Neneng Neneng Nirwana Hendrastuty Nitami Evita Inonu Nosa, Sania Media Nova Evrilia Nugroho Kumala Destianto Nunyai, Reiza Fahlevi Nurul Hidayanah Oktami, Yuga Onassis Yusuf Inonu Palupiningsih, Pritasari Parjito Parjito Pasaribu, A. Ferico Octaviansyah Pasha, Donaya Pendi Pendi Prananta, Gery Prastowo, Kukuh Adi Pratama, Farhan Rizki Priandika, Adhie Thyo Priskilia Lovika Prita Dellia Putri, Nanda Aulia Qadhli Jafar Adrian Qadli Jafar Adrian R Metha, Abhishek Rahayu, Masnia Rahmadany, Loisha Adellia Ramadhan, Surya Reflan Nuari Rendy Ramadhan Retno Triana Reza Kumala Dewi Rido Febriansyah Rika Mersita Rika Mersita Riska Amalia Rohaniah Rohaniah Rojat, Muhamad Randyka Ryan Randy Suryono S. Samsugi Sandi, Yeris Ari Sangha, Zahra Kharisma Sania Media Nosa Sanjaya, Ival Sari, Priskila Lovika Sebastian, Dicky Fernanda Setiawan, Randi Setiawansyah Setiawansyah Setyani, Tria Shynta Octriana Siska Amelia, Siska Siska Febriani Sitna Hajar Hadad Styawati Styawati Suaidah Suaidah Sufiatul Maryana Sufiatul Maryana Sugianto, Rudi Susanti Susanti Syakuru, Nazwa Tauhid, Naufal Tazul Tazul Antoni Umami, Nila Niswatun Untoro Adji Very Hendra Saputra Very Hendra Saputra Waqas Arshad, Muhammad Warsito . Wawan Koeswara Wayan Kresna Yogi Swara yasin, ikbal Yasinta Ismi Yasinta Ismi HS Yeris Ari Sandi Yohanes Simarmata Yosi Khoirunnisa Yulia Indriani Yuliani, Asri Yunita Yunita Yunita Yunita Yuri Rahmanto Yusra Fernando Zaenal Abidin Zahra Kharisma Sangha Zofaisal Hamid, Pratama