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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Sains dan Teknologi Teknik dan Ilmu Komputer Journal of ICT Research and Applications JUITA : Jurnal Informatika Scientific Journal of Informatics Jurnal Teknik Mesin JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Turbo : Jurnal Program Studi Teknik Mesin Techne : Jurnal Ilmiah Elektroteknika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Aptisi Transactions on Technopreneurship (ATT) Computer Science and Information Technologies Jurnal Darma Agung JUNCTO: Jurnal Ilmiah Hukum Jurnal Pengabdian kepada Masyarakat Nusantara ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal Bisnis, Logistik dan Supply Chain Jurnal Pengabdian Teratai Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Makara Journal of Technology Scientific Journal of Informatics Journal of Applied Science and Advanced Engineering
Iwan Setyawan
Universitas Kristen Satya Wacana
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Other

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A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys user Mobile App Baihaqi, Kiki Ahmad; Setyawan, Iwan; Manongga, Danny; Purnomo, Hendryanto Dwi; Hendry, Hendry; Fauzi, Ahmad; Hananto, Aprilia
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.962

Abstract

Data is the most important thing, the use of data can be useful to get an evaluation from the user of a system or application that is built based on mobile. Not only, the assessment or acceptance results of mobile applications during the trial stage are considered important, assessments and comments from direct users are also important things that can be input for mobile application developers. Data mining, or known in English as data mining, is the answer to the process of retrieving data on any media. In this research, data mining is carried out on the media mobile application download service provider Google Playstore, which provides data in the form of comments and ratings. After scraping the data and obtaining the latest data parameters determined by the latest 2000 comments, the data is pre-processed by removing the emot icon character and eliminating unneeded variables so that the data obtained can be processed to the next stage, namely classification based on ratings and sentiment comments. The algorithms used or compared in this research are Support Vector machine, logistic regression and naïve bayes which are known to be reliable in data mining processing. In this research, the accuracy results are 88% for SVM, 90.5% for Logistic Regression and 91% for naïve bayes.
APAKAH KOMITMEN ORGANISASI DAN KETERLIBATAN KERJA MERUPAKAN PREDIKTOR BAGI KINERJA INDIVIDU PADA ORGANISASI NIRLABA? Setyawan, Iwan
Jurnal Bisnis, Logistik dan Supply Chain (BLOGCHAIN) Vol. 1 No. 1 (2021): Jurnal Bisnis, Logistik dan Supply Chain
Publisher : Program Studi Administrasi Bisnis, Institut Bisnis dan Informatika (IBI) Kosgoro 1957

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55122/blogchain.v1i1.227

Abstract

Kendala utama yang dihadapi oleh organisasi nirlaba adalah masalah pendanaan. Ketiadaan sumber dana ini menjadi permasalah dalam upaya meningkatkan kinerja anggota organisasinya. Salah satu upaya yang dapat dilakukan tanpa harus mengeluarkan dana yang besar adalah dengan mendorong anggota organisasi untuk lebih berkomitmen dan aktif terlibat dalam kegiatan keorganisasian. Tujuan penelitian ini untuk menela’ah apakah komitmen organisasi dan keterlibatan kerja merupakan prediktor yang baik bagi peningkatan kinerja anggota pada organisasi nirlaba. Metode penelitian ini menggunakan metode survey dengan kuesioner yang disebarkan secara offline dan online kepada 90 responden yang dipilih secara simple random sampling. Analisis data menggunakan structural equation modeling (SEM) Partial Least Squares (PLS) Second Order Confirmatory dengan bantuan software SmartPLS. Hasil penelitian menemukan komitmen organisasi dan keterlibatan kerja berpengaruh positif dan signifikan terhadap kinerja anggota organisasi nirlaba
Exploring Data Analytics in Attendance Systems: Unveiling Machine Learning Techniques, Patterns, Practices, and Emerging Trends Santoso, Joseph Teguh; Manongga, Danny; Setyawan, Iwan; Purnomo, Hindriyanto Dwi; Hendry
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3438

Abstract

Purpose: The research aims to identify patterns and trends in attendance management through the application of reward and punishment systems as innovative solutions for improving employee attendance and well-being. Methods: This research utilizes a descriptive analysis approach with the application of Machine Learning (ML) techniques to enhance the accuracy of attendance pattern prediction and ML models for the classification of emerging trends and patterns. Research data were obtained through the company's attendance system and divided into two segments (80% for training and 20% for testing) while maintaining a balanced class proportion, then processed using SPSS and Python software with the Scikit-learn library. Result: The results of the study show that employee attendance is increased from 86.52% to 90.44% when the reward and punishment method is applied to the employee attendance system. Proper reward allocation can increase employee motivation to adhere to work schedules and consistently attend, while punishment tends to lead to lower attendance rates. Novelty: This research emphasizes the optimization of attendance management through data analytics approaches and the implementation of advanced technology in attendance systems with the application of ML techniques to analyze attendance data comprehensively and detect significant patterns.
Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection Ridwan, Mohammad; Sembiring, Irwan; Setiawan, Adi; Setyawan, Iwan
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49424

Abstract

Purpose: As the complexity and diversity of cyberattacks continue to grow, traditional security measures fall short in effectively countering these threats within web-based environments. Therefore, there is an urgent need to develop and implement innovative, advanced techniques tailored specifically to detect and address these evolving security risks within web applications.Methods: This research focuses on analyzing attack detection in log access servers using machine learning classification with two primary approaches: expert labeling integration and best model selection. Expert labeling determines whether log entries are safe or indicate an attack.Result: Validation in labeling was applied using different datasets to minimize errors and increase confidence in the resulting dataset. Experimental results show that the Decision Tree and Random Forest models have nearly identical accuracy rates, around 89.3%-89.4%, while the ANN model has an accuracy of 81%.Novelty: This study proposes a fusion of expert knowledge in labeling log entries with a rigorous process of selecting the best classification model. This integration has not been extensively explored in previous research, offering a novel approach to enhancing attack detection within web applications. The research contribution lies in the integration of expert security assessment and the selection of the best model for detecting attacks on server access logs, along with validating labels using various datasets from different log devices to enhance confidence in the analysis results.
Analisa Sistematis Manajemen Pengetahuan Digital Aplikasi Berbasis Kecerdasan Buatan di Universitas Sediyono, Eko; Hasibuan, Zainal Arifin; Setyawan, Iwan; Purnama Harahap, Eka; Darmawan, Arif
ADI Bisnis Digital Interdisiplin Jurnal Vol 3 No 2 (2022): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v3i2.790

Abstract

Melalui kajian literatur yang menyeluruh, analisis ini bertujuan untuk memberikan gambaran luas tentang kajian penggunaan AI di universitas. Temuan deskriptif mengungkapkan bahwa ilmu komputer dan mata pelajaran STEM merupakan mayoritas topik yang tercakup dalam publikasi Artificial Intelligence in Learning (AIL) dan bahwa penelitian empiris paling sering menggunakan pendekatan kuantitatif. Temuan gabungan menunjukkan empat aplikasi untuk AIL dalam layanan kelembagaan dan administrasi dan layanan kontribusi akademik: 1. Metode artikel ini berdasarkan model pembelajaran, algoritma, dan jaringan saraf, yang dapat membuat keputusan tentang jalur pembelajaran individu dan konten siswa, memberikan pijakan kognitif, dan memberikan dukungan kepada siswa untuk berpartisipasi dalam dialog. Masalah penggunaan AIL di universitas dilarang karena persyaratan untuk meningkatkan pendekatan etis dan pedagogis, serta hambatan dan risiko AIL, hubungannya yang rapuh dengan sudut pandang pedagogis teoritis, dan faktor lainnya. Tujuan analisis sistematis adalah untuk memberikan solusi untuk masalah tertentu berdasarkan pendekatan pencarian yang eksplisit, sistematis, dan dapat direproduksi dan kriteria inklusi atau eksklusi yang menentukan penelitian mana yang termasuk pengkodean dan ekstraksi data berikut dari penelitian yang relevan, hasilnya diringkas dan setiap kesenjangan atau inkonsistensi dengan aplikasi nyata disorot. Menjelaskan dampak sifat kognitif dan non-kognitif siswa dalam memprediksi kinerja akademik untuk mahasiswa teknik. Untuk meningkatkan akurasi prediksi, mereka menggunakan faktor non-kognitif seperti mengatur waktu, kepribadian, identitas, kepemimpinan, dan dukungan masyarakat, berbeda dengan banyak studi lain.
Analisis Numerik Multifase Transportasi Slurry Abu Terbang Berkonsentrasi Partikel Tinggi Apriansa, Farul; Ridwan, Ridwan; Setyawan, Iwan; Mulyanto, Tri
TURBO [Tulisan Riset Berbasis Online] Vol 14, No 2 (2025): TURBO: Jurnal Program Studi Teknik Mesin
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/trb.v14i2.4506

Abstract

Transportasi slurry abu terbang mengalami inefisiensi energi akibat resistensi aliran yang tinggi pada pipa konvensional berbentuk lingkaran. Penelitian ini memancarkan pengaruh geometri penampang pipa yaitu lingkaran, spiral dengan empat lobus, dan spiral dengan enam lobus terhadap penurunan tekanan, faktor akurasi, reduksi drag, dan distribusi fraksi volume pada konsentrasi padatan masing-masing 50%, 60%, 65%, 68%, dan 70%. Simulasi Computational Fluid Dynamics (CFD) dilakukan dengan kondisi kecepatan masuk konstan 1.5 m/s. Hasil simulasi menunjukkan bahwa pipa spiral enam lobus secara konsisten memberikan penurunan tekanan dan faktor terjadinya terendah, dengan reduksi drag maksimum sebesar 33,78% pada konsentrasi padatan 70%. Analisis fraksi volume mengindikasikan akumulasi partikel yang lebih signifikan di dekat dasar pipa pada konsentrasi tinggi, yang disebabkan oleh pengendapan gravitasi. Secara keseluruhan, pipa spiral enam lobus menampilkan kinerja hidrolik yang unggul melalui pengurangan resistensi aliran secara efektif, sehingga menjadi alternatif desain yang menjanjikan untuk sistem transportasi slurry dengan konsentrasi tinggi.
Sentiment Analysis of e-Government Service Using the Naive Bayes Algorithm Winny purbaratri; Hindriyanto Dwi Purnomo; Danny Manongga; Iwan Setyawan; Hendry Hendry
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3272

Abstract

E-Government which involves the use of communication and information technology to provide Public services have three obstacles. One of these obstacles is the implementation of e-Government by autonomous regional governments is still carried out individually. Apart from that, implementing the website regions are also not supported by efficient management systems and work processes, this is partly the case This is largely due to the lack of preparation of regulations, procedures and limited resources man. Apart from that, many local governments consider implementing e-Government only involves developing local government websites. More precisely, the implementation of e-Government It is only limited to the maturity stage and ignores the three other important stages that need to be completed. The aim of this research is to determine the level of public approval for government application services. This research uses the Naive Bayes Classifier approach as the methodology. The data sources used in this research consist of user reviews and comments obtained from Google Play Store. The results of this investigation produce a level of precision The highest is achieving a score of 83%. Additionally it shows an accuracy rate of 83%,levelcompleteness is 100%, and F-measure is 90.7%.
Optimizing Thermal Management of Lithium-Ion Batteries Using Mini-Channel Cold Plates: Analysis of Cooling Fluids and Flow Rate Variations using CFD Setyawan, Iwan; Yaqien, Angga Ainul; Ridwan, Ridwan; Sutina, I Wayan; Winarta, Adi
Journal of Applied Science and Advanced Engineering Vol. 4 No. 1 (2026): JASAE: March 2026
Publisher : Master Program in Mechanical Engineering, Gunadarma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59097/jasae.v4i1.72

Abstract

Efficient thermal management is critical for improving the safety, performance, and service life of lithium-ion batteries, especially in electric-vehicle applications. This study evaluates a mini-channel cold-plate system by examining the effects of coolant type, mass flow rate, and channel enhancement on heat dissipation using computational fluid dynamics (CFD) supported by experimental validation. Four fluids, namely water, acetone, ethanol, and methane, were examined at mass flow rates of 1×10-5, 1×10-4, and 1×10-3 kg/s. Among the tested fluids, acetone produced the lowest maximum battery temperature of 28.0 °C at 1×10-3 kg/s, while methane showed the weakest thermal performance. Increasing the mass flow rate consistently reduced battery temperature, but it also increased pressure drop and pumping-power demand. The results indicate that coolant selection should be based not only on thermal performance, but also on pumping-power penalty, safety, and environmental considerations. Although acetone delivered the best cooling performance in this study, its flammability limits its immediate practical adoption. The findings provide design guidance for the development of more effective mini-channel cooling systems for lithium-ion batteries.
Number of Cyber Attacks Predicted With Deep Learning Based LSTM Model Joko Siswanto; Irwan Sembiring; Adi Setiawan; Iwan Setyawan
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20210

Abstract

The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20, batch size 16, and neuron 32 with the lowest evaluation value on MSLE of 0.094, MSE of 9.067, MAE of 2.440, RMSE of 3.010, and MAPE of 10.507 (very good model because the value is less than 15) compared other variations. There is a negative correlation for INTRUSION-MALWARE, BLOCKED-IGNORED, IGNORED-LOGGED, and LOW-MEDIUM. The predicted results for the next 12 months will increase starting from the second month at the same time. The resulting predictions can be used as a basis for policy and strategy decisions by stakeholders in dealing with fluctuations in cyber attacks that occur.
Deep Learning-Based Visualization of Network Threat Patterns Using GAN-Generated Infographic Mars Caroline Wibowo; Iwan Setyawan; Adi Setiawan; Irwan Sembiring
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6717

Abstract

Despite the growing sophistication of cyberattacks, current network traffic analysis tools often lack intuitive visual support, limiting human analysts’ ability to interpret complex threat behaviors. To address this gap, this study proposes a novel deep learning-based visualization framework using a Deep Convolutional Generative Adversarial Network (DCGAN) to synthesize threat-specific infographics from structured numerical features in the CICIDS 2017 dataset. Unlike conventional methods, such as PCA or static dashboards, which often result in abstract or non-adaptive visuals, our approach generates class-distinct grayscale images that preserve the behavioral patterns of various attacks, including denial-of-service, brute force, and port scanning. The preprocessing pipeline reshapes the selected flow-based features into 28×28 matrices to train the generative model. Evaluation using the Frechet Inception Distance (FID) yielded a score of 28.4, whereas a CNN classifier trained on the generated images achieved 91.2% accuracy, confirming visual fidelity and semantic integrity. Additionally, a panel of human experts rated the interpretability of the generated images at 4.3 out of 5.0. These findings demonstrate that generative visualization can enhance human-centered threat analysis by bridging raw data with interpretable imagery, thereby offering a scalable and explainable approach for integrating AI into real-time security workflows.
Co-Authors Adi Setiawan Adi Winarta, Adi Andreas A. Febrianto Andreas Ardian Febrianto Andreas Febrianto Apriansa, Farul April Lia Hananto Ardilla Ayu Dewanti Ridwan Arif Darmawan Baihaqi, Kiki Ahmad Danny Manongga Deddy Susilo Demas Sabatino Deny Christian Dhanar Intan Surya Saputra Eduard Royce Efraim Anggriyono Eko Sediyono Eva Yovita Dwi Utami Farica, Jevan Fauzi Ahmad Muda Fernanda, Denis Aditya Filda Angellia Fransiscus Dalu Setiaji Gunawan Dewantoro Hartanto Kusuma Wardana Henderi . Hendry Heri Setiawan Hindriyanto Dwi Purnomo Ignatius Agus Supriyono Ilham Hizbuloh Irwan Sembiring Ivanna Kristianti Timotius Joko Siswanto Jonatan, Jeany Johana Junias Robert Gultom Kevin Ananta Kuntadi Widiyoko Larasati, Dwira Kurnia Maria Enggar Santika Millenika, Prayudha Mohammad Ridwan Ninda Lutfiani Onix Setyawan, Revivo Priatna , Wowon Purbaratri, Winny Purnama Harahap, Eka Purnomo, Hendryanto Dwi Regina Lionnie Ridwan, Ridwan Romli Jumpai Panggabean Rudi Laksono Santoso, Joseph Teguh Santoso, Yosef Karuna Saptadi Nugroho Sarumaha, Asisman Sembiring, Jenda Suranta Septian Abednego Simanjuntak, Sarida Hotdeliana Simbolon, Winda C Sinaga, Ester Ronida Sirilus Widi Surya Pranata Sukoco, Septyan Eko Hardyan Saputra Sulistio Sulistio Sutina, I Wayan Theodorus Leo Hartono Theopillus J. H. Wellem Tri Mulyanto Tri Wahyuningsih Trisno Sri Suparyati Soenarto dan Dibyo Pramono Agung Wibowo Untung Rahardja Wibowo, Mars Caroline Winny purbaratri Yaqien, Angga Ainul Yayi Suryo Prabandari Yulianto, Eko Susetyo Zainal Arifin Hasibuan Zalukhu, Pasrah