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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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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.
Multi-Class Facial Acne Classification using the EfficientNetV2-S Deep Learning Model Pramono, Aldi Yogie; Kusnawi, Kusnawi
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3157

Abstract

Acne vulgaris is a common dermatological condition that significantly impacts psychosocial well-being, particularly among adolescents and young adults. Accurate identification of acne lesion types is crucial for effective treatment planning, yet manual assessment by dermatologists is subjective and resource-intensive. This study proposes a Convolutional Neural Network (CNN)-based approach using EfficientNetV2-S with transfer learning and data augmentation to perform multi-class classification of five acne lesion types: blackheads, whiteheads, papules, pustules, and cysts. The model was trained and evaluated on 4,673 annotated facial images, achieving an accuracy of 96.66%, outperforming conventional lightweight CNNs and achieving comparable results to heavier ensemble architectures. Statistical validation using p-values and effect sizes confirms the model’s robustness. The scientific contribution of this research lies in the integration of EfficientNetV2-S with a customized classification head optimized for multi-class acne recognition—an area underexplored in dermatological AI research. Unlike previous works focusing on binary classification or ensemble models, our approach offers a lightweight, accurate, and scalable solution for real-world teledermatology, thus establishing a novel benchmark in multi-class acne classification.
Implementation of Blockchain for Integrated Civil Service Statistical Data (Case Study: Civil Service and Human Resource Development Agency of Madiun Regency, East Java Province) Huda, Syaiful; 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.
Integration of BERT-VAD, MFCC-Delta, and VGG16 in Transformer-Based Fusion Architecture for Multimodal Emotion Classification Nayoma, Fisan Syafa; Kusnawi, Kusnawi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4915

Abstract

Emotion is a condition that plays an important role in human interaction and is the main focus of intelligence research in utilizing multimodal. Previous studies have classified multimodal emotions but are still less than optimal because they do not consider the complexity of human emotions as a whole. Although using multimodal data, the selection of feature extraction and the merging process are still less relevant to improving accuracy. This study attempts to categorize emotions and improve precision through a multimodal methodology that utilizes Transformer-based Fusion. The data used consists of a synthesis of three modalities: text (extracted through BERT and assessed through the affective dimensions of NRC Valence, Arousal, and Dominance), audio (extracted through MFCC and delta-delta2 from the RAVDESS and TESS datasets), and images (extracted through VGG16 on the FER-2013 dataset). The model is built by mapping each feature into an identical dimensional representation and processed through a Transformer block to simulate the interaction between modalities, known as feature-level interactions. The classification procedure is run through a dense layer with softmax activation. Model evaluation was performed using Stratified K-Fold Cross Validation with k=10. The evaluation results showed that the model achieved 95% accuracy in the ninth fold. This result shows a significant improvement from previous research at the feature level (73.55%), and underlines the effectiveness of the combination of feature extraction and Transformer-based Fusion. This study contributes to the field of emotion-aware systems in informatics, facilitating more adaptive, empathetic, and intelligent interactions between humans and computers in practical applications.
Evaluating Classification Models for Predicting Product Success in Indonesian E-Commerce Aulya, Fiola Utri; Kusnawi, Kusnawi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5071

Abstract

The intense competition within the Indonesian e-commerce landscape presents a significant challenge for sellers in forecasting product performance. This study offers a unique contribution by systematically comparing seven machine learning classification algorithms to predict product success across Indonesia's three largest platforms: Shopee, Tokopedia, and Lazada. The primary objective is to identify the most effective algorithm for predicting whether a product's sales will surpass the market median. The methodology involved aggregating and preprocessing a dataset of 3,673 product listings. Product success was defined as a binary variable based on sales volume exceeding the dataset's median. Seven models, including Logistic Regression, KNN, SVM, and tree-based ensembles like Random Forest, XGBoost, and LightGBM, were trained and optimized using a 5-fold cross-validated GridSearchCV. Evaluation was based on accuracy, ROC AUC, and F1-score. The results demonstrate a clear performance hierarchy, with tree-based ensemble models achieving superior results. Random Forest emerged as the premier model, attaining an accuracy of 83.2% and an AUC of 0.907. A subsequent feature importance analysis revealed that shop_followers and price were the most significant predictors of success. This finding has crucial practical implications, particularly for Micro, Small, and Medium Enterprises (MSMEs), by providing a data-driven framework for decision-making. The model enables them to focus resources on actionable strategies—building seller reputation and optimizing pricing—to enhance their competitiveness effectively.
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 Atin Hasanah Atmoko, Alfriadi Dwi Aulya, Fiola Utri BAYU SATRIYA, RIYAN Bhahari, Rifqi Hilal Candra Rusmana Cynthia Widodo Dede - Sandi Dede Husen Dede Sandi Dewi Kartika Dimaz Arno Prasetio Elsa Virantika Ema Utami Erna Utami Fajar Abdillah, Moh Fajar Aji Prayoga Haris, Ruby Hartatik Haryo, Wasis Hasirun Hasirun Hasirun, Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Huda, Luthfi Nurul Indra Irawanto Irawanto, Indra Joang Ipmawati Kanoena, Melcior Paitin Karisma Septa Kresna Khairullah, Irfan Khalil Khoerul Anam, Khoerul Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusirini Kusrini KUSRINI Kusrini Kusrini Kusrini - - Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini M Andika Fadhil Eka Putra M. Nurul Wathani Maehendrayuga, Arief Majid Rahardi Malik, Husni Hidayat Maringka, Raissa Mashuri, Ahmad Sanusi Mellany, Juventania Sheva 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 Prema Adhitya Dharma Kusumah 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