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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 Journal of Information Systems and Informatics Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, and 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 Bulletin of Network Engineer and Informatics (BUFNETS) 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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IMPLEMENTASI MOORA PADA SELEKSI DOSEN TERBAIK BERDASARKAN HASIL PENILAIAN DALAM PEMBELAJARAN KULIAH Hasirun Hasirun; Kusrini Kusrini; Kusnawi Kusnawi
Indonesian Journal of Business Intelligence (IJUBI) Vol 6 No 1 (2023): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v6i1.3331

Abstract

Lecturer performance assessment is one of the activities of monitoring and evaluating performance with the aim of supervising the learning process and ensuring that lecturers carry out their duties in accordance with policies and teaching materials that have been determined. Lecturer performance assessment is carried out by students at the end of each semester by assessing lecturers based on criteria related to lecture learning. The criteria assessed in college learning are learning aspects, technological proficiency, integrity, and inspiration. The results of the student assessment will be reported to the learning development and quality assurance institution, which can later be used to determine the best lecturer performance. In this research, we apply the MOORA method to help determine the best lecturer based on assessment results in lecture reasoning. In its implementation, the MOORA method performs calculations based on criteria and weight values that have been determined and produces a ranking that can be used to determine the best lecturer's performance. In this study, the highest ranking was on the VPB alternative with a final value of 0.138, while the lowest value was on the DAM alternative with a final value of 0.108.
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

Catfish is a fish that is consumed by many Indonesians and is one of the business opportunities. This can be a good prospect for the future with catfish farming. Even many restaurants and restaurants use this fish as one of the menus. Cultivating catfish is not easy, there are many parameters that need to be considered so that fish life can develop properly. Starting from sufficient water ph or according to the standard of living catfish, up to the air temperature. These factors will affect if catfish farms are ignored. Timely feeding is also a major factor that can have sufficient nutrition. With an automatic fish feed system and water quality monitoring, farmers' problems related to water quality can be monitored using sensors. Feeding can be done via a telegram bot with certain settings as needed.
An An Explainable Machine Learning Approach Using Random Forest and SHAP for Employee Attrition Prediction Joang Ipmawati; 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.
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 Tegar Wirawan; 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.
Chili Leaf Disease Classification Using Transfer Learning with VGG16 and MobileNetV2 Combined with Random Search Hyperparameter Tuning Aryawijaya; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17383224

Abstract

Chili is one of the main food commodities in Indonesia with considerable economic value. Frequent climate changes have made chili plants more vulnerable to pest and disease attacks. Early identification of these diseases is crucial, as delays can lead to crop failure. However, this process presents its own challenges, as it requires specific expertise and considerable time. This study employs the transfer learning method using the VGG16 and MobileNetV2 architectures to build a model capable of classifying diseases in chili plants based on leaf images, along with the use of Random Search hyperparameter tuning to improve model accuracy. The results show that the use of transfer learning for disease classification achieved high accuracy, with MobileNetV2 reaching an accuracy score of 88% without tuning. Meanwhile, the application of Random Search hyperparameter tuning proved effective in improving model accuracy, particularly with the VGG16 architecture, which saw a significant accuracy increase from 51% to 89%. It can be concluded that the transfer learning method is well-suited for identifying diseases in chili plants based on leaf images with high accuracy, and that the application of Random Search hyperparameter tuning successfully enhanced the model’s performance.
Water Quality Analysis and Consumption Feasibility Using Support Vector Machine and CatBoosting with Hyperparameter Tuning Christa Putri Rahayu; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17342085

Abstract

Water quality analysis plays an important role in determining the suitability of water for human consumption. This study aims to build a machine learning model that is able to classify water quality based on several parameters such as pH, hardness, solids content, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The dataset used comes from Kaggle with a total of 3,276 sample data. The two main algorithms applied in this study are Support Vector Machine (SVM) and CatBoost. The research process includes data preprocessing, data balancing using SMOTE, modeling, and model performance evaluation. Hyperparameter tuning is applied to both algorithms to improve performance. The results show that CatBoost has the best performance with an accuracy of 95.8% after hyperparameter tuning, compared to SVM which achieved an accuracy of 77.9%. In addition, CatBoost excels in all evaluation metrics, including precision, recall, and F1-score.
Implementation of Blockchain for Integrated Civil Service Statistical Data (Case Study: Civil Service and Human Resource Development Agency of Madiun Regency, East Java Province) Syaiful Huda; Kusrini Kusrini; Kusnawi Kusnawi
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.12170

Abstract

Digital transformation in personnel data management demands a transparent, secure, and integrated system to support data-driven decision-making and enhance accountability in personnel services. An integrated information system and personnel statistical data are necessary to assist leaders in analyzing staffing needs and making more accurate and efficient data-based policies, while also strengthening the principles of good governance through improved transparency and accountability. Therefore, the Personnel and Human Resource Development Agency of the Government of Madiun Regency, East Java, requires technology capable of effectively managing personnel information by offering security, transparency, and data integrity through a decentralized mechanism. Blockchain, as a distributed ledger technology, provides an innovative solution for maintaining data integrity and increasing public trust through permanent, encrypted, and validated transaction records within a decentralized network. The implementation of blockchain in the management of personnel statistical data remains limited, despite the technology’s ability to support real-time audit trails and reliable interactive data visualization. This study proposes a framework for integrating a relational database with smart contracts on the Ethereum network, by recording the hash of statistical data in the smart contract as proof of data authenticity. Data is retrieved from the database, hashed, and the hash is stored in the smart contract to ensure its integrity, with the results visualized in interactive charts. This framework is expected to improve transparency, accountability, and trust in personnel statistical data to support more accurate and efficient strategic decision-making.
Implementasi Algoritma Naïve Bayes Dalam Mengidentifikasi Jenis Penyakit Cacar Dengan Image Processing Yudha Bagas Pattimura; Melcior Paitin Kanoena; Anggit Dwi Hartanto; Hartatik Hartatik; Kusnawi Kusnawi
Intechno Journal : Information Technology Journal Vol. 5 No. 1 (2023): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2023v5i1.1571

Abstract

Cacar merupakan salah satu penyakit kulit yang sering diderita banyak masyarakat mulai dari anak bayi sampai orang tua. Cacar memliki beberapa jenis yang antara lain adalah cacar air (Quipperian), cacar api (herpes zoster) dan cacar monyet, seluruh penyakit ini semuanya dapat menular ke seama manusia melalui kontak lansung, bersin, batuk atau tersentuh dengan isi gelembung cacar yang pecah. Minimnya pengetahuan masyarakat dan tidak adanya penyuluahan dari pemerintah membuat masyarakat tidak mengetahui akan perbedaan jenis-jenis cacar yang diderita dan dapat terjadinya kesalahan dalam pengobatan. Dalam penilitian ini kami menggunakan image processing dengan metode histogram untuk ekstraksi fitur tekstur cacar tersebut serta menggunakan dengan metode klasifikasi naïve bayes dalam mengklasifikasi jenis cacar yang diderita oleh pasien. Dari penilitian yang kami lakukan menunjukan bahwa mengklasifikasi nilai ekstraksi fitur tekstur citra cacar dengan metode naïve bayes memperolehonilai akurasi sebesar 75%.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Yusuf Ainnur Rafli Ainul Yaqin Aldi Yogie Pramono Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Ardiansyah, Fachri Arief Maehendrayuga 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 Christa Putri Rahayu 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 Hakiki, Muhammad Ridhwan Haris, Ruby Hartatik Haryo, Wasis Hasanah, Atin Hasirun Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Huda, Luthfi Nurul Husni Hidayat Malik Indana Zulfa Indra Surya Permana Irawanto, Indra Joang Ipmawati Joang Ipmawati Joang Ipmawati Juventania Sheva Mellany Karisma Septa Kresna Karisma Septa Kresna Khairullah, Irfan Khalil Khoerul Anam, Khoerul Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusrini Kusrini, Kusirini Ledyvia Audiz Coranov M Andika Fadhil Eka Putra M. Nurul Wathani Majid Rahardi Malik, Husni Hidayat Maringka, Raissa Mashuri, Ahmad Sanusi Melcior Paitin Kanoena Mochamad Agung Wibowo Mochamad Agung Wibowo Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Husein Budiraharjo Muhammad Irvan Shandika Muhammad Irvan Shandika Muhammad Reza Riansyah Nayoma, Fisan Syafa Neni Firda Wardani Tan Ngaeni, Nurus Sarifatul Nurul Zalza Bilal Jannah Olajuwon, Sayyid Muh. Raziq Omar Muhammad Altoumi Alsyaibani Pandiangan, Van Daarten Pebri Antara Pitaloka, Nadhira Triadha Prastyo, Rahmat Pringandana, Cokorda Gde Lanang Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Raffa Nur Listiawan Dhito Eka Santoso RAMADHAN, SYAIFUL Ridwan Sanjaya Ridwan Sanjaya Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun RIYAN BAYU SATRIYA 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 Tegar Wirawan Teguh Arlovin Wahyu Pujiharto, Eka Wangsa, Sabda Sastra Wibowo , Mochamad Agung Widodo, Cynthia Widyanto, Agung Wirawan, Tegar Yudha Bagas Pattimura Yusa, Aldo Yusrinnatul Jinana triadin Yuza, Adela Zaenul Amri