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All Journal JAIS (Journal of Applied Intelligent System) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Progresif: Jurnal Ilmiah Komputer Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Pengabdian Mandiri Jurnal Satya Informatika Proceeding International Pelita Bangsa SAINTEK Jurnal Pengabdian Nasional (JPN) Indonesia
Edy Widodo
Universitas Pelita Bangsa
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Comparison of K-Means and K-Medoid Algorithms in Classifying Village Status (Case Study: Gorontalo Province) Aswan Supriyadi Sunge; Nanang Tedi Kurniadi; Edy Widodo
Proceeding International Pelita Bangsa Vol. 1 No. 01 (2023): September 2023
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/pipb.v1i01.2675

Abstract

In the national development process, the village occupies a very important position. This is because it is the smallest government structure and has direct contact with the community. Seeing the importance of its role in national development, one of which is Gorontalo Province, based on directions from the central government, is trying to implement the Village Fund Allocation (ADD) policy for all villages in Gorontalo Province. In distributing the allocation of funds, it is necessary to map the status of the Village to find out the amount that must be given. This test uses the average execution time and the Davies Bouldin Index (DBI). After testing it is known that the K-Medoid Algorithm has a better DBI value than the K-Means Algorithm with the DBI value of the K-Medoid Algorithm being 0.050. On the other hand, the K-Means Algorithm has a better average execution time than the K-Medoid Algorithm, where the average execution time is 1 second.
Analisis Sentimen Ulasan Hotel Zuri Express Lippo Cikarang Menggunakan Algoritma Naive Bayes Dion Marcelino; Edy Widodo; Dodit Ardiatma
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3671

Abstract

The hospitality industry relies heavily on customer satisfaction, which is often reflected through reviews on online travel applications. Hotel Zuri Express Lippo Cikarang requires an automated sentiment monitoring system to enhance its service quality. This study aims to implement the Naive Bayes Classifier algorithm to classify customer review sentiments into positive and negative categories. The research dataset consists of 1,637 reviews obtained via web scraping from the google maps. The research methodology includes text preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) weighting, and N-Gram (Bigram) feature modeling. The experimental results demonstrate that the model achieves an accuracy rate of 91.16%, with a precision of 0.91 and a recall of 0.99 for the majority class. Keyword analysis using Word clouds identified cleanliness and staff service as the primary factors driving customer satisfaction. Despite challenges in detecting negative sentiments within an imbalanced dataset, the Naive Bayes algorithm proved to be reliable and efficient for automated sentiment analysis, providing a robust tool to support fast and accurate management decision-making.Keywords: Sentiment Analysis; Customer Reviews; Hotel Zuri Express; Naive Bayes; Text mining.AbstrakIndustri perhotelan sangat bergantung pada kepuasan pelanggan yang tercermin melalui ulasan aplikasi travel online. Hotel Zuri Express Lippo Cikarang memerlukan sistem otomatis untuk memantau sentimen guna meningkatkan kualitas layanan. Penelitian ini bertujuan mengimplementasikan algoritma Naive Bayes Classifier dalam mengklasifikasikan sentimen ulasan pelanggan ke kategori positif dan negatif. Data penelitian mencakup 1.637 ulasan hasil web scraping dari google maps. Tahapan penelitian meliputi preprocessing teks, pembobotan kata Term Frequency-Inverse Document Frequency (TF-IDF), serta pemodelan fitur N-Gram (Bigram). Hasil pengujian menunjukkan model menghasilkan tingkat akurasi sebesar 91,16%, dengan nilai presisi 0,91 dan recall 0,99 pada kelas mayoritas. Analisis kata kunci melalui Word cloud mengidentifikasi aspek kebersihan dan pelayanan staf sebagai faktor utama kepuasan pelanggan. Meskipun terdapat tantangan dalam mendeteksi sentimen negatif pada dataset tidak seimbang, algoritma Naive Bayes terbukti handal dan efisien dalam melakukan analisis sentimen otomatis untuk mendukung pengambilan keputusan manajemen hotel secara cepat dan akurat.Kata kunci: Analisis Sentimen; Ulasan Pelanggan; Hotel Zuri Express; Naive Bayes; Text mining.
Optimasi Prediksi Diabetes Mellitus Menggunakan Komparasi Random Forest dan SVM dengan Analisis Pemilihan Fitur Berbasis SHAP Aswan Supriyadi Sunge; Dendy K. Pramudito; Abdul Halim Anshor; Edy Widodo
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Diabetes Mellitus merupakan masalah kesehatan di dunia yang sangat signifikan maka dari itu dibutuhkan prediksi dini dan akurat. Penelitian ini bertujuan untuk mengoptimalkan prediksi dengan membandingkan model Machine Learning (ML) dengan Random Forest dan Support Vector Machine, yang ditingkatkan dengan analisis SHAP (SHapley Additive exPlanations) untuk mencari fitur tertinggi atau berpengaruh. Penelitian ini menggunakan dataset yang terdiri dari 1000 data pasien dengan 14 fitur, dan 1 kelas. Preprocessing melibatkan pembersihan data dan duplicate, dilanjutkan dengan testing dan training data, dan hasil pengujian dengan model Random Forest mendapatkan akurasi 99%, sementara SVM mencapai 86%, lalu pengujian analisis SHAP mengungkapkan bahwa Age, Urea dan Kreatinin adalah fitur yang paling berpengaruh dari fitur yang lainnya. Hasil analisis perbandingan menunjukkan bahwa mengungguli dalam hal akurasi prediksi secara keseluruhan, dan ini sangat berkontribusi pada peningkatan metode prediksi yang optimal dan sebagai parameter klinis utama untuk diagnosis.
Penerapan Data Mining Menggunakan Metode Naïve Bayes Untuk Menentukan Faktor Yang Mempengaruhi Kelulusan Dan Ketidaklulusan Mahasiswa Di Universitas Pelita Bangsa Edy Widodo; Ditya Lambang Setyawan
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Data mining is the process of discovering patterns from datasets to generate information that can be used for prediction based on historical data. This study aims to analyze the factors influencing student graduation and non-graduation at Universitas Pelita Bangsa using the Naïve Bayes method. Data processing was conducted through manual calculations, Microsoft Excel, and RapidMiner, producing consistent evaluation results with an accuracy of 68.18%, precision of 33.33%, and recall of 16.67%. The findings indicate that the Naïve Bayes method can be effectively applied to predict student graduation factors with acceptable accuracy, making it a suitable approach for analyzing graduation data and supporting academic decision-making processes.
Sistem Pakar Diagnosa Kerusakan Komputer Dengan Metode Case Based Reasoning Pada Toko Click Komputer Karawang Edy Widodo; Anisa Devi Asmara
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Computer technicians often require a significant amount of time to diagnose computer damage, and in many cases, the process is delayed while they determine the appropriate solution. To address this issue, an expert system can be developed to provide faster and more accurate diagnoses of computer problems. This study applies the Case Based Reasoning (CBR) method, an artificial intelligence approach that solves new problems by referring to solutions from previous similar cases. By comparing current symptoms with stored cases in the system, appropriate recommendations can be generated efficiently. The system is developed using the PHP programming language, while MySQL is utilized as the database for storing case data and solutions. Through this implementation, the expert system is expected to assist technicians in identifying types of computer damage quickly, precisely, and accurately. As a result, it can reduce diagnostic time, improve work efficiency, and support technicians in delivering proper handling and effective solutions for computer-related issues.
Implementasi Algoritma Neural Network Untuk Menentukan Tingkat Kepuasan Pelanggan di Perusahaan Retail Edy Widodo; Muhamad Rizki Sahputra
Jurnal SIGMA Vol 14 No 4 (2023): Desember 2023
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v14i4.7316

Abstract

Dalam penelitian ini dirumuskan masalah bagaimana menganalisa kualitas perusahaan retail terhadap kepuasan pelanggannya dengan menggunakan metode Neural Network dikarenakan banyak masukan masukan dari pelanggan di sosial media yang berpotensi akan menjadi penurunan sales dan hilangnya kualitas pelayanan terhadap perusahaan retail. Penelitian ini bertujuan untuk mengetahui apakah Teknik Data Mining dengan algoritma neural network ini dapat digunakan untuk tingkat kepuasan pelanggan. Serta mendapatkan mengenai accuracy, precision dan recall yang didapat saat melakukan pengujian data kualitas ini menggunakan algoritma Neural Network. Penelitian ini menggunakan Teknik dan tahapan – tahapan pada data mining untuk klasifikasi data kepuasan pelanggan. Pengolahan data menggunakan tool Rapid Miner, hasil penelitian data menyatakan bahwa tingkat Accuracy akan lebih baik jika data yang diolah semakin banyak, yaitu untuk tingkat accuracy 81,82%. Begitupun juga dengan tingkat precision dan recall akan lebih baik jika data yang diolah lebih besar. Berdasarkan penelitian yang dilakukan dapat disimpulkan bahwa Teknik Data Mining dengan algoritma neural network dapat digunakan untuk melakukan Analisa kepuasan pelanggan.
Explainable DDoS Detection with a CNN-LSTM Hybrid Model and SHAP Interpretation Amali Amali; Anggi Muhammad Rifa'i; Edy Widodo; Ahmad Turmudi Zy; Dhani Ariatmanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6865

Abstract

The rising frequency and complexity of Distributed Denial of Service (DDoS) attacks pose a severe threat to network security. This study aims to develop an effective and interpretable DDoS detection framework using a hybrid deep learning approach. The proposed method integrates Convolutional Neural Networks (CNN) to capture local traffic patterns and Long Short-Term Memory (LSTM) networks to model temporal dependencies. The CICIDS 2017 dataset, after preprocessing steps including data cleaning, standardization, and class balancing with SMOTE, was used to train and evaluate the model. Experimental results show that the framework achieved 99.98% accuracy and a 99.83% F1-Score, with minimal false positive and false negative rates. This study integrates SHAP to improve model interpretability, aligning feature importance with network security expertise. Future research will focus on real-time deployment, cross-dataset validation, and exploring alternative explainable AI techniques for improved scalability.
Penerapan dan Pelatihan Sistem Pembayaran Sekolah di SMK Brahari sebagai Upaya Transformasi Layanan Keuangan Sekolah Ismasari Nawangsih; Tri Ngudi Wiyatno; Edy Widodo; Eko Budiarto; Annisa Maulana Majid
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 7 No. 1 (2026): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jpni.v7i1.1669

Abstract

Digital transformation in the education sector demands increased efficiency and transparency in school financial management. However, many schools, including SMK Brahari, still rely on manual payment systems that are prone to errors and lack transparency. The identified gap is the absence of an integrated and user-friendly digital payment system accessible to all stakeholders. This community service activity aimed to implement and provide training on a digital school payment system for 20 administrative and educational staff. The methods included initial observation, development of a simple system, hands-on training, and implementation assistance. Evaluation results showed that 85% of participants understood the system workflow, 90% found it helpful in simplifying their tasks, and all participants expressed readiness to implement it. The system proved effective in improving efficiency, transparency, and ease of payment monitoring, marking an important step toward transforming school financial services.
PENERAPAN CONVOLUTIONAL NEURAL NETWORK PADA PENGENALAN BAHASA ISYARAT INDONESIA SECARA REAL-TIME Rifki Ryan Maulana; Abdul Kholiq; Edy Widodo
JURNAL SATYA INFORMATIKA Vol. 10 No. 2 (2025): JURNAL SATYA INFORMATIKA
Publisher : FAKULTAS TEKNIK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59134/jsk.v10i2.763

Abstract

Sign language is a form of visual communication used by individuals who are deaf or speech-impaired. However, many people in the general public still lack understanding of sign language, which hinders communication between people with disabilities and their surroundings. This research aims to develop a real-time alphabet translator system for Indonesian Sign Language (BISINDO), implemented as an Android application. The system utilizes a Convolutional Neural Network (CNN) model based on the MobileNetV2 architecture, which is trained to recognize 26 alphabet letters from hand gesture images sized 128x128 pixels in RGB format. The dataset was collected and processed through augmentation and divided into training, validation, testing, and evaluation sets. The model was trained using transfer learning and fine-tuning methods and then converted into TensorFlow Lite (.tflite) format for deployment on Android devices. Evaluation results show that the model achieved an average accuracy of 93% on the evaluation dataset. Testing the Android application also demonstrated good real-time performance in recognizing hand gestures. This application is expected to help bridge communication between people with disabilities and the general public through practical and accessible technology.
Co-Authors Abdul Halim Anshor Abdul Kholiq Agus Sulistiawan Ahmad Jaelani Ahmad Turmudi Zy al fiyan Amali Amali Ananto Tri Sasongko Anggi Muhammad Rifa'i Anisa Devi Asmara Annisa Maulana Majid Antika Zahrotul Kamalia Asep Arwan Sulaeman Aswan S Sunge Aswan Supriyadi Sunge Dendy K. Pramudito Dhani Ariatmanto Dion Marcelino Ditya Lambang Setyawan Dodit Ardiatma Eko Budiarto Gatot Tri Pranoto Muhamad Fatchan Muhamad Rizki Sahputra Nanang Tedi Kurniadi Nawangsih, Ismasari Riani Riani Rifki Ryan Maulana Sifa Fauziah Tri Ngudi Wiyatno