Article Per Year (5 Year)
p-Index From 2021 - 2026
12.39
P-Index
This Author published in this journals
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

Published : 117 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search
Journal : building of informatics technology and science

 1 
Modification of the Grey Relational Analysis Method in Determining the Best Mechanic Arshad, Muhammad Waqas; Sulistiani, Heni; Maryana, Sufiatul; Palupiningsih, Pritasari; Rahmanto, Yuri; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5678

Abstract

Determining the best mechanics in the industry has an important role to ensure the quality and reliability of the products and services offered. Competent and experienced mechanics are able to diagnose and repair accurately and efficiently, thereby minimizing operational downtime and increasing productivity. Without a structured system, mechanical performance appraisals tend to be subjective and inconsistent, which can lead to dissatisfaction among employees and customers. Mechanics may not get clear and constructive feedback on their performance, thus hindering skill development and professionalism. The purpose of the research of the modified Grey Relational Analysis (GRA) using standard deviation is to improve the accuracy and reliability of the decision-making process in situations where the data has a high degree of variability or significant uncertainty. By integrating standard deviations into the GRA, the study aims to account for variations and fluctuations in the data, which allows for more accurate and representative assessment of the criteria. This modification is expected to overcome the weaknesses of traditional GRAs that may not adequately consider data uncertainty, as well as produce more robust and realistic alternative rankings. The results of the best ranking of mechanics, Mechanic FR ranks first with a value of 0.11, followed by Mechanic HS with a value of 0.104. The third place was occupied by Mechanic AY with a score of 0.099.
Modifikasi Metode Simple Additive Weighting Dalam Rekomendasi Restoran Terbaik Berdasarkan Ulasan Pengunjung Prastowo, Kukuh Adi; Sulistiani, Heni; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5679

Abstract

Simple Additive Weighting (SAW) is a method in DSS that is used to solve multi-criteria problems by adding up the value weights of each alternative. The weakness of SAW is its sensitivity to weight determination and value which can significantly affect the final result. If the weight or value of the criteria is not determined correctly or does not reflect reality well, the results of the decision can be less accurate. The purpose of this study is to modify the SAW method with the name SAW-C to be more effective in providing the best restaurant recommendations based on visitor reviews. SAW modification using a change driven approach not only improves accuracy in decision-making, but also improves adaptability and responsiveness to dynamic and complex environments. The SAW-C method not only improves decision-making accuracy, but also improves adaptability and responsiveness to dynamic and complex environments. SAW-C integrates flexibility and adaptability in managing changes in visitor preferences or the weighting of relevant criteria, which often change over time. With this approach, the recommendation system can dynamically update restaurant ratings based on recent reviews and changing visitor preferences, providing more personalized and relevant recommendations. The results of the ranking of the best restaurants using the SAW-C method show that the results of rank 1 with a final score of 0.92135 are obtained by Flamboyant Restaurant, rank 2 with a final score of 0.70548 obtained by Zozo Garden, and rank 3 with a final score of 0.70312 obtained by Square Restaurant.
Komparasi Random Forest dan Artificial Neural Network dalam Prediksi Dampak AI terhadap Pekerjaan 2030 Jefri Jaka Tirta; Heni Sulistiani
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9716

Abstract

The development of Artificial Intelligence (AI) is expected to affect the future employment structure, particularly regarding automation risks in 2030 as a period of accelerated AI adoption across various industrial sectors. This study aims to compare the performance of the Random Forest and Artificial Neural Network (ANN) algorithms in predicting the impact of AI on jobs. The study employed two modeling approaches, namely regression to predict job automation probability and classification to determine job risk categories into Low, Medium, and High classes through a discretization process. The dataset was obtained from Kaggle with a total of 3,000 records and processed through preprocessing, feature engineering, and train-test splitting with an 80:20 ratio. Regression evaluation was conducted using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²), while classification evaluation used accuracy and F1-score. The results showed that Random Forest achieved the best regression performance with an MAE of 0.0786, RMSE of 0.0932, and R² of 0.8640, outperforming ANN with an MAE of 0.0949, RMSE of 0.1137, and R² of 0.7973. In the classification task, both algorithms achieved an accuracy and F1-score of 99.33%. This study shows that Random Forest is more stable on tabular data and contributes to the comparative analysis of ensemble learning and neural network approaches for predicting the impact of AI on jobs.
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.
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 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