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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bina Insani ICT Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika Technomedia Journal Applied Technology and Computing Science Journal Building of Informatics, Technology and Science Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) GUYUB: Journal of Community Engagement Informatics, Electrical and Electronics Engineering Journal Of Artificial Intelligence And Software Engineering Jurnal Informatika: Jurnal Pengembangan IT El-Mujtama: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science

iwan Syarif

Politeknik Elektronika Negeri Surabaya

Author-ID : 559699

Religion Aerospace Engineering Humanities 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Analisis Kinerja Algoritma Machine Learning Untuk Klasifikasi Potensi FRAUD Klaim Layanan Kesehatan Rumah Sakit Ubed, Imanullah Ali; Syarif, Iwan; Saputra, Ferry Astika
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7349

Fraud in healthcare claims represents a critical challenge that undermines the efficiency and sustainability of Indonesia's National Health Insurance (JKN) system. This study contributes a large-scale comparative evaluation of five machine learning algorithms for classifying potential fraud in BPJS Kesehatan claims, namely Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), XGBoost + SMOTE, and Logistic Regression (LR). A novelty of this study lies in applying the SMOTE technique in conjunction with XGBoost to address class imbalance in fraud datasets. The dataset consists of over 200,000 claim entries, which have undergone data cleaning, normalization, and feature selection. Performance was assessed using precision, recall on fraud class (positive), f1-score, accuracy, and confusion matrix visualizations to capture classification error distribution. Results demonstrate that ANN and XGBoost + SMOTE are superior in detecting fraudulent claims with high recall, while SVM achieves the most balanced performance in terms of precision and sensitivity. Random Forest and Logistic Regression serve as moderate baselines but are less effective in identifying complex fraud patterns. This study contributes to the development of a more adaptive and efficient fraud detection system based on machine learning, with practical implications for strengthening the automatic verification system used by BPJS Kesehatan.

THD Minimization in Seven-Level Packed U-Cell (PUC) Inverter using Particle Swarm Optimization Amran, Osamah Abdullah Yahya; Windarko, Novie Ayub; Syarif, Iwan
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3352

This study presents the modeling and simulation of an asymmetric seven-level Packed U-Cell (PUC) multilevel inverter employing a reduced number of power switches. A Modified Pulse Width Modulation (MPWM) scheme, optimized through the Particle Swarm Optimization (PSO) algorithm, is implemented to determine the optimal switching angles for enhanced harmonic elimination. The primary objective is to improve the output voltage waveform quality while reducing Total Harmonic Distortion (THD) and enhancing switching efficiency. The novelty of this work lies in integrating PSO with MPWM control in an asymmetric seven-level PUC inverter configuration with fewer switches, a combination that has not been previously addressed. Simulation results in Simulink demonstrate that the proposed PSO-optimized MPWM strategy achieves a THD of 17.72%, outperforming conventional modulation techniques. These findings highlight the effectiveness of intelligent optimization methods for multilevel inverter control and their potential contribution to improving power quality in renewable energy applications.

Optimasi Sistem Antrian Digital Terintegrasi Fitur Chat pada Layanan Kelurahan Gebang Putih Berbasis User-Centered Design Aziz, Adam Shidqul; Mubtadai, Nur Rosyid; Permatasari, Desy Intan; Saputra, Ferry Astika; Syarif, Iwan; Fariza, Arna; Al Rasyid, M. Udin Harun; Ramadijanti, Nana; Sumarsono, Irwan; Ahsan, Ahmad Syauqi; Sa'adah, Umi; Yunanto, Andhik Ampuh; Kusuma, Selvia Ferdiana; Primajaya, Grezio Arifiyan; Edelani, Renovita; Khoirunnisa, Asy Syaffa; Al Falah, Adam Ghazy
El-Mujtama: Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): El-Mujtama: Jurnal Pengabdian Masyarakat
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

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

Digital transformation in the public sector has encouraged the development of queuing systems that not only organize service flows but also provide faster and more accurate access to information for citizens. Gebang Putih Urban Village, a community partner of the Electronic Engineering Polytechnic Institute of Surabaya (PENS), implemented a digital queuing system based on Android and web platforms in 2024; however, the system still had limitations in terms of responsiveness and communication channels with citizens. This community service activity focuses on the technical optimization of the queuing system by integrating a chat-based interaction feature into the mobile application and simplifying the queue number retrieval flow through conversational interaction. The system was developed using a User-centered design (UCD) approach, with key stages including needs analysis based on the evaluation of the previous system, design of a new system architecture, implementation of chat integration, and internal testing with urban village officers as key users. The optimization results cover three main technical aspects: (1) integration of the chat service module into the mobile application without modifying the core queuing logic, (2) a queue number retrieval feature via chat that is directly connected to the digital queuing module, and (3) a two-way chat channel between citizens and officers facilitated through a web-based dashboard. Internal trials indicate improvements in service workflows, reduced face-to-face interactions for simple inquiries, and increased staff understanding of how to use the digital system. From a community engagement perspective, this work represents a development and capacity-building phase that strengthens officers’ ability to manage technology-based services and provides a technical foundation that is ready to be disseminated and replicated in other urban villages with minimal adaptation.

Dimensionality Reduction Algorithms on High Dimensional Datasets Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 2 No 2 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicatedespecially when the number of possible different combinations of variables is so high. In this research, we evaluate the performance of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as feature selection algorithms when applied to high dimensional datasets.Our experiments show that in terms of dimensionality reduction, PSO is much better than GA. PSO has successfully reduced the number of attributes of 8 datasets to 13.47% on average while GA is only 31.36% on average. In terms of classification performance, GA is slightly better than PSO. GAâ€ reduced datasets have better performance than their original ones on 5 of 8 datasets while PSO is only 3 of 8 datasets.Keywords: feature selection, dimensionality reduction, Genetic Algorithm (GA), Particle Swarm Optmization (PSO).

Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 4 No 2 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

This paper describes the advantages of using Evolutionary Algorithms (EA) for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS) are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA) and Particle Swarm Optimizations (PSO) as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN) as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets). However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time.

Data Mining Approach for Breast Cancer Patient Recovery Fahrudin, Tresna Maulana; Syarif, Iwan; Barakbah, Ali Ridho
EMITTER International Journal of Engineering Technology Vol 5 No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Breast cancer is the second highest cancer type which attacked Indonesian women. There are several factors known related to encourage an increased risk of breast cancer, but especially in Indonesia that factors often depends on the treatment routinely. This research examines the determinant factors of breast cancer and measures the breast cancer patient data to build the useful classification model using data mining approach.The dataset was originally taken from one of Oncology Hospital in East Java, Indonesia, which consists of 1097 samples, 21 attributes and 2 classes. We used three different feature selection algorithms which are Information Gain, Fisherâ€™s Discriminant Ratio and Chi-square to select the best attributes that have great contribution to the data. We applied Hierarchical K-means Clustering to remove attributes which have lowest contribution. Our experiment showed that only 14 of 21 original attributes have the highest contribution factor of the breast cancer data. The clustering algorithmdecreased the error ratio from 44.48% (using 21 original attributes) to 18.32% (using 14 most important attributes).We also applied the classification algorithm to build the classification model and measure the precision of breast cancer patient data. The comparison of classification algorithms between NaÃ¯ve Bayes and Decision Tree were both given precision reach 92.76% and 92.99% respectively by leave-one-out cross validation. The information based on our data research, the breast cancer patient in Indonesia especially in East Java must be improved by the treatment routinely in the hospital to get early recover of breast cancer which it is related with adherence of patient.

Influence of Logistic Regression Models For Prediction and Analysis of Diabetes Risk Factors Maulana, Yufri Isnaini Rochmat; Badriyah, Tessy; Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Diabetes is a very serious chronic. Diabetes can occurs when the pancreas doesn't produce enough insulin (a hormone used to regulate blood sugar), cause glucose in the blood to be high. The purpose of this study is to provide a different approach in dealing with cases of diabetes, that's with data mining techniques mengguanakan logistic regression algorithm to predict and analyze the risk of diabetes that is implemented in the mobile framework. The dataset used for data modeling using logistic regression algorithm was taken from Soewandhie Hospital on August 1 until September 30, 2017. Attributes obtained from the Hospital Laboratory have 11 attribute, with remove 1 attribute that is the medical record number so it becomes 10 attributes. In the data preparation dataset done preprocessing process using replace missing value, normalization, and feature extraction to produce a good accuracy. The result of this research is performance measure with ROC Curve, and also the attribute analysis that influence to diabetes using p-value. From these results it is known that by using modeling logistic regression algorithm and validation test using leave one out obtained accuracy of 94.77%. And for attributes that affect diabetes is 9 attributes, age, hemoglobin, sex, blood sugar pressure, creatin serum, white cell count, urea, total cholesterol, and bmi. And for attributes triglycerides have no effect on diabetes.

Arrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate Assodiky, Hilmy; Syarif, Iwan; Badriyah, Tessy
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Arrhythmia is a heartbeat abnormality that can be harmless or harmful. It depends on what kind of arrhythmia that the patient suffers. People with arrhythmia usually feel the same physical symptoms but every arrhythmia requires different treatments. For arrhythmia detection, the cardiologist uses electrocardiogram that represents the cardiac electrical activity. And it is a kind of sequential data with high complexity. So the high performance classification method to help the arrhythmia detection is needed. In this paper, Long Short-Term Memory (LSTM) method was used to classify the arrhythmia. The performance was boosted by using AdaDelta as the adaptive learning rate method. As a comparison, it was compared to LSTM without adaptive learning rate. And the best result that showed high accuracy was obtained by using LSTM with AdaDelta. The correct classification rate was 98% for train data and 97% for test data.

Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization Tahir, Muhlis; Badriyah, Tessy; Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Preeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria. Â The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing.

Spatio Temporal with Scalable Automatic Bisecting-Kmeans for Network Security Analysis in Matagaruda Project Hisyam, Masfu; Barakbah, Ali Ridho; Syarif, Iwan; S, Ferry Astika
EMITTER International Journal of Engineering Technology Vol 7 No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Internet attacks are a frequent occurrence and the incidence is always increasing every year, therefore Matagaruda project is built to monitor and analyze internet attacks using IDS (Intrusion Detection System). Unfortunately, the Matagaruda project has lacked in the absence of trend analysis and spatiotemporal analysis. It causes difficulties to get information about the usual seasonal attacks, then which sector is the most attacked and also the country or territory where the internet attack originated. Due to the number of unknown clusters, this paper proposes a new method of automatic bisecting K-means with the average of SSE is 93 percents better than K-means and bisecting K-means. The usage of big spark data is highly scalable for processing massive data attack.

Co-Authors Adam Prugel-Bennett Afifah, Izza Nur Agung Muliawan Ahsan, Ahmad Syauqi Aidil Saputra Kirsan Aidil Saputra Kirsan Aji , Rendra Suprobo Al Falah, Adam Ghazy Alfaqih, Wildan Maulana Akbar Ali Ridho Barakbah Alwan Fauzi Amalia Wirdatul Hidayah Amran, Osamah Abdullah Yahya Andhik Ampuh Yunanto APRIANDY, KEVIN ILHAM Ardhani, Misbahul Arna Fariza Assodiky, Hilmy Assodiky, Hilmy Aziz, Adam Shidqul Bagas Dewangkara Bima Sena Bayu Dewantara Binti Kholifah Dadet Pramadihanto Daisy Rahmania Syarif Darmawan, Zakha Maisat Eka Desy Intan Permatasari, Desy Intan Deyana Kusuma Wardani Dian Neipa Purnamasari Dimas Bagus Santoso Dona Wahyudi Dzulfiqar, Achmad Fakhri Edelani, Renovita Edi Satriyanto Entin Martiana Kusumaningtyas Fahrudin, Tresna Maulana Fahrudin, Tresna Maulana Fakhri, Haidar Fathoni, Kholid Fauzy, Aryazaky Iman Ferry Astika Saputra Ferry Astika Saputra Fitri Setyorini Gary Wills Gunawan, Agus Indra Hamida, Silfiana Nur Hardiyanti, Fitriani Rohmah Hasan Basri Hidayah, Amalia Wirdatul Hidayah, Nadila Wirdatul Hisyam, Masfu Hisyam, Masfu Huda, Achmad Thorikul Idris Winarno Khoirunnisa, Asy Syaffa Kholifah, Binti Kindarya, Fabyan Kusuma, Selvia Ferdiana M Udin Harun Al Rasyid, M Udin Harun Mahardhika, Yesta Medya Maulana, Yufri Isnaini Rochmat Maulana, Yufri Isnaini Rochmat Mayangsari, Mustika Kurnia Mufid, Mohammad Robihul Mufid, Mohammad Robihul Muhammad Fajrul Falah Muhlis Tahir Nadila Wirdatul Hidayah Nana Ramadijanti, Nana Ningrum, Ayu Ahadi Novie Ayub Windarko Nur Rosyid Mubtadai, Nur Rosyid Nur Sakinah Nur Ulima Rusmayani Prasetyo Primajaya, Grezio Arifiyan Rabiatul Adawiyah Rachmawati, Oktavia Citra Resmi Reesa Akbar Rengga Asmara Rengga Asmara Riyanto Sigit, Riyanto Rizky Yuniar Hakkun Rosmaliati, Rosmaliati Rozie, Fachrul Rudi Kurniawan Rulisiana Widodo S, Ferry Astika S, Ferry Astika Sa'adah, Umi Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Shabirin, Irsal Sritrusta Sukaridhoto Sudaryanto, Aris Sumarsono, Irwan Susanti, Puspasari Tahir, Muhlis Tessy Badriyah, Tessy Tri Harsono Ubed, Imanullah Ali Utomo, Agus Priyo Walujo, Ivana Yudith Wibowo, Prasetyo Willy Sandhika

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