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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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Komparasi Algoritma Supervised Learning dan Feature Selection pada Klasifikasi Penyakit Gagal Jantung Kusnawi, Kusnawi; Khrisna Irham Fadhil Pratama
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3487

Abstract

Penyakit gagal jantung merupakan penyakit yang mematikan yang ada di dunia, gagal jantung terjadi karena kondisi atau adanya kelainan otot-otot pada jantung. Pada tahun 2021 data yang ada pada WHO kematian dikarenakan penyakit jantung mencapai 17,8 juta jiwa. Salah satu cara yang dapat dilakukan yaitu dengan klasifikasi dengan menggunakan dataset public kaggle. Penelitian ini bertujuan mengkomparasi algoritma supervised learning dan metode feature selection yang terbaik, guna memperoleh hasil analisis data dengan akurasi yang baik dalam klasifikasi. Penerapan algoritma SVM, KNN, Naïve Bayes tanpa menggunakan feature selection algoritma SVM unggul menghasilkan accuracy 88.41%. Penerapan forward selection pada algoritma SVM, KNN, Naïve Bayes, algoritma SVM unggul dengan nilai accuracy 89.86%. Penerapan pearson corellation pada algoritma SVM, KNN, Naïve Bayes, algoritma KNN unggul menghasilkan accuracy 90.58%. Penerapan feature selection baik forward selection dan pearson corellation mampu meningkatkan performa akurasi, akan tetapi penerapa pearson corellation pada penelitian ini lebih baik dalam meningkatkan akurasi.
Ekspresi Emosi Berdasarkan Suara Menggunakan Algortima Multi Layer Perceptron dan Support Vector Machine Qurniaty, Charlen Alta; Kusnawi, Kusnawi
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3567

Abstract

Rapid developments in voice-based emotion recognition have made positive contributions to human-computer interaction. This research aims to compare the performance of two algorithms, namely Multilayer Perception (MLP) and Support Vector Machine (SVM), in recognizing emotions based on sound. The data used in this research was taken from Kaggle, which amounted to 1440 voice data. The data is then collected into several emotions which will then be feature extracted from the dataset to eliminate irrelevant information and reduce noise so that the classification results are optimal. The research results show that the classification accuracy using the Multilayer Perception (MLP) algorithm reaches 83%, while the Support Vector Machine (SVM) reaches 82%. Based on the accuracy results of both methods, it can be concluded that the Multilayer Perception algorithm is superior to the Support Vector Machine algorithm in the context of voice-based emotion recognition. Keyword: Emotional Expression, Voice, Mfcc, Multi-Layer Perceptron, Support Vector Machine
Pemanfaatan Analisis Sentimen Terhadap Kasus Bunuh Diri Mahasiswa Menggunakan Naïve Bayes Classifier Ainnur Rafli; Kusnawi, Kusnawi
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3605

Abstract

Suicide is currently a serious problem in higher education, especially among university students, and special approaches and attention are required to prevent it. With today's advances in technology, emotion analysis techniques can be an effective way to understand students' feelings and thoughts that may lead to suicidal behavior or indicate a risk of suicide. For this study, we scraped the data for his 1,151 tweets on Twitter and cleaned it up to 817. Of these, there are 745 negative tweets and 72 positive tweets. Additionally, the data is implemented in an algorithm that performs a data split of 80:20 with an accuracy of 90,24%. That's the "depression" that often appears when visualizing Lata data. Especially in Indonesia, there are many suicides due to depression. The purpose of this study is to understand the factors associated with student suicide and to determine the effectiveness and accuracy of this algorithm. Additionally, this study is expected to provide insights into educational and mental health settings to improve prevention strategies and more effective approaches
Sentiment Analysis and Classification of Forest Fires in Indonesia Irawanto, Indra; Widodo, Cynthia; Hasanah, Atin; Dharma Kusumah, Prema Adhitya; Kusrini, Kusirini; Kusnawi, Kusnawi
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1337.175-185

Abstract

Twitter is a well-known social media platform since it allows users to retweet, leave comments, exchange the latest information, and even find out about forest fires. However, no one has processed Twitter data in the form of the topic of forest fires. Despite the fact that this information is incredibly important for determining how much people care about sharing this knowledge and this phenomenon. Hence, one of the efforts in managing Twitter data in the form of text is using NLP (Natural Language Processing) which is now starting to be widely discussed. In addition, the use of word weighting utilizing Vader will also be used in this process. Furthermore, the use classifying process is conducted using 3 kinds of algorithms including Naïve Bayes, Random Forest and SVM (Support Vector Machine). The results of this study, the accuracy obtained from each method has not reached 90%. The Precision, Recall and F1-Score values have also not reached 90%.
An An Explainable Machine Learning Approach Using Random Forest and SHAP for Employee Attrition Prediction Ipmawati, Joang; Kusnawi, Kusnawi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3281

Abstract

Understanding and predicting employee attrition is a strategic challenge for modern organizations because high turnover rates impact operational costs, productivity, and the loss of valuable company knowledge. Conventional statistical approaches, such as logistic regression, have limitations in capturing complex and non-linear relationships between workforce variables. This study proposes an Explainable Machine Learning approach by integrating the Random Forest algorithm and the SHAP (SHapley Additive Explanations) method to predict and interpret employee attrition behavior more transparently.  However, existing HR analytics research rarely combines tree-based ensemble models with robust explainability, creating a gap in developing accurate yet interpretable solutions.The dataset used is HR-employee-attrition, with 1,470 entries and 35 features covering demographics, compensation, and job satisfaction. After preprocessing and parameter optimization, the Random Forest model achieved 83% accuracy, an ROC-AUC of 0.789, and a PR-AUC of 0.414. Model performance was validated through a 70:30 stratified split supported by cross-validation to ensure predictive consistency, indicating good classification performance despite class imbalance. SHAP analysis identified five key features influencing attrition: OverTime, MonthlyIncome, Age, YearsAtCompany, and JobSatisfaction. Unlike conventional black-box models, the proposed approach provides global and local explanations that clarify the contribution of each feature to individual predictions. Practically, these insights enable HR departments to identify high-risk employees earlier and design targeted retention interventions based on data-driven evidence.The findings demonstrate that integrating Random Forest with SHAP produces models that are both accurate and interpretable. Future research may explore integrating SHAP explanations into interactive HR decision-support systems and evaluating more advanced explainable deep learning methods.
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