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All Journal Bulletin of Electrical Engineering and Informatics Nuansa Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Universitas Batanghari Jambi JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Informatika Universitas Pamulang JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah Aisyah Journal of Informatics and Electrical Engineering Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Respati Jurnal Abdi Insani JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Journal of Computer System and Informatics (JoSYC) Jurnal Graha Pengabdian Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration TEPIAN Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA SENADA : Semangat Nasional Dalam MengabdI Journal of Electrical Engineering and Computer (JEECOM) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sisfotek Global Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia SENADA : Semangat Nasional Dalam Mengabdi TECHNOVATAR Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Jurnal Teknik AMATA Jurnal TAM (Technology Acceptance Model)
Kusnawi Kusnawi
Universitas Amikom Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences 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 Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Penerapan Particle Swarm Optimization (PSO) dalam Pemilihan Parameter Secara Otomatis pada Support Vector Machine (SVM) untuk Prediksi Kelulusan Mahasiswa Politeknik Amamapare Timika Sepriadi Bumbungan; Kusrini; Kusnawi
Jurnal Teknik AMATA Vol. 4 No. 1 (2023)
Publisher : Politeknik Amamapare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55334/jtam.v4i1.77

Abstract

Ketepatan waktu kelulusan mahasiswa, memiliki pengaruh terhadap kualitas Perguruan Tinggi karena merupakan salah satu indikator penilaian dalam proses akreditasi Perguruan Tinggi, sehingga memprediksi kelulusan mahasiswa dianggap penting untuk dilakukan. Prediksi kelulusan dapat dilakukan dengan menggunakan teknik data mining. Salah satunya dengan Support Vector Machine (SVM) yang memiliki performa yang baik dalam melakukan klasifikasi, karena dapat menyelesaikan masalah overfitting, data training yang sedikit, dan lambatnya konvergensi. Namun, Support Vector Machine (SVM) masih memiliki kekurangan, dalam hal komputasi data dengan jumlah yang besar dan dalam pemilihan parameter secara optimal. Untuk hal itu, diperlukan algoritma Particle Swarm Optimization (PSO) dalam pemilihan parameter yang sesuai pada metode Support Vector Machine (SVM). Eksperimen pada metode Support Vector Machine (SVM) dan Particle Swarm Optimization (PSO) dilakukan dengan optimasi parameter Gamma, C, dan Epsilon. Software yang digunakan untuk mendapatkan hasil eksperimen yaitu RapidMiner 9.10. Hasil eksperimen dengan menggunakan algoritma Support Vector Machine (SVM) menghasilkan nilai accuracy, recall, precision, dan F1 score masing-masing dengan nilai 93,33%, 91,04%, 98,39%, dan 94,57%. Sementara itu, Optimasi algoritma Support Vector Machine (SVM) dengan menggunakan Particle Swarm Optimization (PSO) menghasilkan nilai accuracy, recall, precision, dan F1 score masing-masing dengan nilai 98,02%, 98,55%, 98,08%, dan 98,31%. Berdasarkan hasil eksperimen yang telah dilakukan, dinyatakan terbukti bahwa penerapan algorima Particle Swarm Optimization (PSO) dapat meningkatkan kinerja dari algoritma Support Vector Machine (SVM).
Performance Analysis of SVM and Random Forest Algorithms in the Case of the Influence of Music on Mental Health Karisma Septa Kresna; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 2 (2025): April
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15130408

Abstract

Mental health disorders are conditions that impress a person's behavior, mindset, and emotions. According to WHO data, the rate of mental disorders in Asia has increased significantly in the past two decades, with about one-fifth of the world's adolescent population experiencing stress each year. Music has long been known to have a positive influence on mental health, and music therapy is used as one approach to assist individuals in improving social, mental, and physical conditions. In this study, the authors used data mining techniques to identify relevant patterns regarding the influence of music on mental health. Two classification algorithms, namely the Support Vector Machine (SVM) and Random Forest, is used to analyze and characterize the data. SVM is known to excel at managing high-dimensional data, while Random Forest is effective at handling data with missing outliers and features. This study purpose to oppose the performance of the two algorithms in classifying the influence of music on mental health to identify the superior algorithm in this context. The Random Forest algorithm gets 93% accuracy and SVM gets 95% accuracy, the hyperparameter tuning on the SVM algorithm has a better performance than Random Forest with an accuracy score of 97% for SVM, while for Random Forest it gets an accuracy score of 94%. The results of the study are expected to provide insight into the use of music as a mental health therapy tool.
Performance Analysis of Support Vector Machine and Gradient Boosting Machine Algorithms for Heart Disease Prediction Wirawan, Tegar; Kusnawi, Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 2 (2025): April
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15126239

Abstract

Cardiovascular disease ranks among the primary causes of mortality globally, with death rates rising each year. Assessing heart disease risk is crucial for enhancing the efficiency of prevention and treatment strategies. This study seeks to evaluate the effectiveness of two machine learning techniques, namely Support Vector Machine and Gradient Boosting Machine, in forecasting heart disease using a dataset obtained from Kaggle. The research process starts with gathering data, followed by exploratory analysis, preprocessing through label encoding, handling class imbalance with SMOTE, and normalizing data using Standard Scaler. Features were selected using the Correlation Thresholding method. Subsequently, the dataset was divided into training and testing sets to develop predictive models. The model performance was assessed using evaluation metrics, including accuracy, precision, recall, and F1-Score. The findings indicate that the Gradient Boosting Machine outperformed the Support Vector Machine, achieving an accuracy of 98% compared to SVM's accuracy of 93%. This research is expected to contribute to healthcare practices by enabling early detection of heart disease risks. Future research is recommended to explore other algorithms or employ more diverse datasets to achieve better results
Optimization of Stress Classification Among Students Using Random Forest Algorithm Raffa Nur Listiawan Dhito Eka Santoso; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 2 (2025): April
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15130385

Abstract

Stress is a condition of physical and psychological discomfort experienced by students due to academic pressure, demands from parents and teachers, and schoolwork. This stress can lead to physical tension, behavioral changes, and mental health problems if not handled properly.  Random Forest is a promising approach to analyze and classify student stress. The aim of this study is to classify stress among students to enable the development of targeted interventions to support student well-being and academic success. The dataset used was sourced from Kaggle and included 1100 datasets with 21 columns. The research stages included data preprocessing, exploratory data analysis, modeling, Decision tree classification and evaluation of the confusion matrix model and Deployment as a measure of stress level. Classification results were evaluated by calculating accuracy, precision, recall and f1-score for stress classes (low, medium and high). The results of this study resulted in an accuracy value before tuning of 87.27% and after tuning of 88.64%. This research can provide insights for schools, parents, and government to develop more effective strategies in addressing the problem of stress among students. The use of Random Forest algorithm is proven to be effective in analyzing and classifying stress, so that it can help in decision making and appropriate welfare interventions to tackle before stress reaches critical levels.
Comparison of the Performance of Multiple Linear Regression and Multi-Layer Perceptron Neural Network Algorithms in Predicting Drug Sales at Pharmacy XYZ Arifuddin, Danang; Kusrini, Kusrini; Kusnawi, Kusnawi
JURNAL SISFOTEK GLOBAL Vol 15, No 1 (2025): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v15i1.15822

Abstract

The needs of better drugs management tool especially that can predict specific drugs consumption volume are needed by any healthcare facility including retail pharmacies. Thus, finding better prediction algorithm with suitable variable internally and externally becoming this research objectives. The research compares correlation score and histogram of each predictor variable with target variable and further input the selected variable into MLR and MLPNN algorithm to find recommended algorithm with better MSE and MAPE. The findings indicate that MLPNN with backpropagation method slightly outperforms MLR with ‘h-7’ as single input variable but with unstable predictions with lower MSE of 19588 and MAPE of 22,3%. While MLR's MSE of 22346,129 and MAPE of 25.4% with ‘h-7’ and ‘bm’ as input variable perform stable prediction. Finally, the research find ‘h-7’ is the most significant variable among other variables and both MLR and MLPNN are both need better improvement to perform drugs prediction analysis.
Design of Automatic Feeder with Adjustable Temperature, PH, and Weather for Catfish Pebri Antara; Ema Utami; Kusnawi Kusnawi
Cerdika: Jurnal Ilmiah Indonesia Vol. 5 No. 4 (2025): Cerdika: Jurnal Ilmiah Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/cerdika.v5i4.2473

Abstract

This study aims to analyze the influence of service quality, tax collection strategies, and tax sanctions on taxpayer compliance in paying Land and Building Tax (PBB-P2) in Bekasi City. Data were collected through questionnaires distributed to 60 land and building taxpayers. The study employed multiple linear regression analysis using SPSS version 27 to process the data. The results showed that service quality had a positive and significant effect on taxpayer compliance, with a t-value of 2.083 greater than the t-table value of 2.002. Conversely, the tax collection strategy showed a t-value of 0.649, which is less than the t-table value, indicating no significant effect on taxpayer compliance. Meanwhile, tax sanctions had a t-value of 6.577, exceeding the t-table value, demonstrating a significant positive impact on compliance. Additionally, the F-test resulted in a value of 32.519, suggesting that when analyzed simultaneously, service quality, tax collection strategies, and tax sanctions collectively have a positive influence on taxpayer compliance. These findings highlight the importance of effective service delivery and enforcement mechanisms to enhance tax compliance for PBB-P2 in Bekasi City
Perbandingan Performansi Algoritma Multiple Linear Regression dan Multi Layer Perceptron Neural Network dalam Memprediksi Penjualan Obat: Comparison of the Performance of Multiple Linear Regression Algorithms and Multi Layer Perceptron Neural Networks in Predicting Drug Sales Arifuddin, Danang; Kusrini, Kusrini; Kusnawi, Kusnawi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1952

Abstract

Penelitian ini mengevaluasi pemilihan atribut dari variabel internal (jumlah penjualan) dan eksternal (cuaca, harga komoditas, inflasi) menggunakan metode korelasi, serta membandingkan performansi algoritma Multiple Linear Regression (MLR) dan Multi-Layer Perceptron Neural Network dengan backpropagation (MLPNN-b) dalam memprediksi penjualan obat analgesik di “Apotek XYZ”. Metrik evaluasi Mean Squared Error (MSE) dan Mean Absolute Percentage Error (MAPE) digunakan untuk mengukur akurasi prediksi. Hasil menunjukkan bahwa atribut internal "h-7" memiliki korelasi tertinggi (0,35) terhadap penjualan harian, sementara variabel eksternal seperti suhu harian, harga bawang merah, dan suku bunga juga memberikan kontribusi. Algoritma MLPNN-b dengan parameter tertentu mencapai MAPE 22,3% dan MSE 19.588 pada atribut tunggal, sedangkan MLR memiliki kinerja lebih merata pada atribut kombinasi dengan MAPE 25,6% dan MSE 22.768. Namun, kedua model masih mengalami underfitting dengan tingkat kesalahan prediksi yang cukup tinggi. Penelitian ini menyimpulkan bahwa meskipun MLPNN lebih unggul dalam menangkap hubungan non-linear dibandingkan MLR, akurasi prediksi masih belum optimal. Oleh karena itu, eksplorasi model hybrid serta integrasi lebih banyak variabel eksternal direkomendasikan untuk meningkatkan prediksi penjualan dan mendukung sistem manajemen stok farmasi yang lebih akurat.
Random Search Optimization Using Random Forest Algorithm For Liver Disease Prediction BAYU SATRIYA, RIYAN; Kusnawi, Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15468679

Abstract

The liver is a vital human organ with complex and diverse functions. One of the diseases that affect the liver is hepatitis or liver disease. Early detection is crucial to enable more effective intervention and slow the progression of the disease. However, diagnosing liver disease often faces challenges, especially in detecting the early stages of the disease from complex and diverse medical data. This study aims to optimize the Random Forest algorithm using the Random Search method for liver disease detection. The Random Forest algorithm is applied as the primary model in this research, while hyperparameter optimization is performed using the Random Search method to enhance model performance. The results show that the Random Forest model without optimization achieves an accuracy of 93%. After hyperparameter optimization, the model's accuracy increases to 94%. In conclusion, applying hyperparameter optimization using the Random Search method successfully improves the performance of the Random Forest model. The resulting model provides more accurate predictions.
AI Web-based Computer Service Management System at PUSCOM Muhammad Irvan Shandika; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16280893

Abstract

This research aims to develop a web-based computer service management system with artificial intelligence (AI) integration at PUSCOM to address challenges in manual service management, such as customer data recording, service status tracking, and report generation. The problems faced by PUSCOM include potential data errors, loss of physical documents, and delays in performance evaluation due to manual processes. The research method used is the Agile SDLC approach, covering problem identification, data collection through interviews and documentation, functional and non-functional requirements analysis, system modeling using UML, NoSQL Firebase database design, interface design, implementation using Next.js and Javascript, and AI chatbot integration using Vercel AI SDK with the Google Gemini model. The research results demonstrate the successful development of a system capable of automating data recording, facilitating online service registration, managing products, and providing an AI chatbot to assist admins in report generation and real-time damage analysis. This system is proven to enhance operational efficiency, reduce manual errors, and support strategic decision-making at PUSCOM, contributing to improved service quality and customer satisfaction.
TESTING OF PIJAR SEKOLAH APPLICATION WITH LOAD TESTING METHOD USING LOCUST Prastyo, Rahmat; Kusrini, Kusrini; Kusnawi, Kusnawi
Jurnal TAM (Technology Acceptance Model) Vol 16, No 1 (2025): Jurnal TAM (Technology Acceptance Model)
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v16i1.1790

Abstract

The Pijar Sekolah application is an application created to help the learning and teaching process. Pijar Sekolah has several features, such as attendance, assignments, exams, and grades. The feature that is widely accessed by users is the exam feature. Therefore, when many users use the application simultaneously, it is important to conduct performance test on the Pijar Sekolah application. This study purpose is to conduct performance test with the Load Testing method using Locust. Test was carried out with a gradually increasing number of users, the number of users as testers were 50, 100, 200, 400, and 800 with a ramp-up period of 1 second. The testing will be carried out in accordance with the examination process carried out using the Pijar Sekolah application by accessing Login, Login Status, Exam List, Start Exam, Question List, Exam Questions, and Submit Answers.  The results of the test show that the performance in terms of response time is stable when testing from 50 to 400, but in RPS (Request Per Second) the average value increases with the number of 800 users getting an average value of 33.83 RPS. However, the test with the number of 400 users get an error in submitting answers. When test with the number of users 800, the response time increases and there are several errors by getting responses of 502 and 422 for 0.033%. The results of this study can be used to determine which processes need to be improved in performance. So that the Pijar Sekolah application can be used by many schools in carrying out the exam process simultaneously.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Yusuf Ainnur Rafli Ainul Yaqin Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Anggit Dwi Hartanto, Anggit Dwi Ardiansyah, Fachri Arief Setyanto Arifuddin, Danang Arnila Sandi Aryawijaya Asadulloh, Bima Pramudya Assani, Moh. Yushi Atin Hasanah Atmoko, Alfriadi Dwi Aulya, Fiola Utri BAYU SATRIYA, RIYAN Bhahari, Rifqi Hilal Candra Rusmana Dede - Sandi Dede Husen Dede Sandi Dewi Kartika Dharma Kusumah, Prema Adhitya Dimaz Arno Prasetio Elsa Virantika Ema Utami Erna Utami Fajar Abdillah, Moh Fajar Aji Prayoga Haris, Ruby Hartatik Haryo, Wasis Hasanah, Atin Hasirun Hasirun Hasirun, Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Huda, Luthfi Nurul Irawanto, Indra Joang Ipmawati Kanoena, Melcior Paitin Karisma Septa Kresna Khairullah, Irfan Khalil Khoerul Anam, Khoerul Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusrini Kusrini, Kusirini M Andika Fadhil Eka Putra M. Nurul Wathani Majid Rahardi Malik, Husni Hidayat Maringka, Raissa Mashuri, Ahmad Sanusi Mochamad Agung Wibowo Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Husein Budiraharjo Muhammad Irvan Shandika Muhammad Reza Riansyah Nayoma, Fisan Syafa Neni Firda Wardani Tan Ngaeni, Nurus Sarifatul Nurul Zalza Bilal Jannah Omar Muhammad Altoumi Alsyaibani Pandiangan, Van Daarten Pattimura, Yudha Bagas Pebri Antara Pitaloka, Nadhira Triadha Pramono, Aldi Yogie Prastyo, Rahmat Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Rahayu, Christa Putri RAMADHAN, SYAIFUL Ridwan Sanjaya Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun Rizal Khadarusman Rodney Maringka Rohim, Ni’matur saifulloh Saifulloh, saifulloh Salman Alfaris Salman Alfaris, Salman San Sudirman Sekarsih, Fitria Nuraini Sentoso, Thedjo Sepriadi - Bumbungan Sepriadi Bumbungan Sri Yanto Qodarbaskoro Sry Faslia Hamka Sudirman, San Suyatmi Suyatmi Suyatmi Suyatmi Syaiful Huda Syaiful Ramadhan Tamuntuan, Virginia Taryoko, Taryoko Teguh Arlovin Wahyu Pujiharto, Eka Wangsa, Sabda Sastra Widodo, Cynthia Widyanto, Agung Wirawan, Tegar Yusa, Aldo Yusrinnatul Jinana triadin Yuza, Adela Zaenul Amri