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All Journal Journal of Information Systems Engineering and Business Intelligence RABIT: Jurnal Teknologi dan Sistem Informasi Univrab KOMPUTIKA - Jurnal Sistem Komputer Jurnal Digit : Digital of Information Technology Academic Journal of Computer Science Research (AJCSR) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Technologia Journal Integrated System and Management Technology
Ednawati Rainarli
Universitas Komputer Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Perbandingan Simple Logistic Classifier dengan Support Vector Machine dalam Memprediksi Kemenangan Atlet Ednawati Rainarli; Arif Romadhan
Journal of Information Systems Engineering and Business Intelligence Vol. 3 No. 2 (2017): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (178.812 KB) | DOI: 10.20473/jisebi.3.2.87-91

Abstract

Abstrak— Prediksi kemenangan atlet adalah hal yang harus dilakukan oleh pelatih ketika memutuskan pemain  yang akan diturunkan dalam suatu pertandingan. Banyaknya faktor-faktor yang mempengaruhi kemenangan atlet membuat keputusan tersebut tidak mudah untuk ditentukan. Dalam penelitian ini akan dilakukan perbandingan dari penggunaan metode Simple Logistic Classifier (SLC) dengan Support Vector Machine (SVM)  dalam memprediksi kemenangan atlet berdasarkan data kesehatan dan data latihan fisik. Data yang digunakan diambil dari 28 cabang olahraga perorangan. Rata-rata akurasi SLC dan SVM masing-masing diperoleh sebesar 80% dan 88%, sedangkan rata-rata kecepatan pemrosesan metode SLC dan SVM adalah 1,6 detik dan 0,2 detik.  Hal ini menunjukkan bahwa penggunaan metode SVM lebih unggul daripada SLC, baik dari segi kecepatan maupun dari nilai akurasi yang dihasilkan. Selain pengujian akurasi, dilakukan pula pengujian terhadap 24 fitur yang digunakan dalam proses klasifikasi.  Hasilnya diketahui bahwa pengurangan fitur melalui tahap seleksi mengakibatkan penurunan nilai akurasi. Berdasarkan hal tersebut disimpulkan bahwa semua fitur yang digunakan dalam penelitian ini adalah fitur yang berpengaruh dalam penentuan prediksi kemenangan atlet. Kata Kunci— Prediksi, Simple Logistic Classifier, Sports Data Mining, Support Vector MachineAbstract— A coach must be able to select which athlete has a good prospect of winning a game.  There are a lot of aspects which influence the athlete in winning a game, so it's not easy by coach to decide it.This research would compare Simple Logistic Classifier (SLC) and Support Vector Machine (SVM) usage applied to predict winning game of athlete based on health and physical condition record.  The data get from 28 sports. The accuracy of SLC and SVM are 80% and 88% meanwhile processing times of SLC and SVM method are 1.6 seconds dan 0.2 seconds.The result shows the SVM usage superior to the SLC both of speed process and the value of accuracy.  There were also testing of 24 features used in the classifications process. Based on the test,  features selection process can cause decreasing the accuracy value. This result concludes that all features used in this research influence the determination of a victory athletes prediction. Keywords— Prediction, Simple Logistic Classifier, Sports Data Mining, Support Vector Machine
Implementasi Q-Learning dan Backpropagation pada Agen yang Memainkan Permainan Flappy Bird Ardiansyah; Ednawati Rainarli
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 1: Februari 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1479.852 KB)

Abstract

This paper shows how to implement a combination of Q-learning and backpropagation on the case of agent learning to play Flappy Bird game. Q-learning and backpropagation are combined to predict the value-function of each action, or called value-function approximation. The value-function approximation is used to reduce learning time and to reduce weights stored in memory. Previous studies using only regular reinforcement learning took longer time and more amount of weights stored in memory. The artificial neural network architecture (ANN) used in this study is an ANN for each action. The results show that combining Q-learning and backpropagation can reduce agent’s learning time to play Flappy Bird up to 92% and reduce the weights stored in memory up to 94%, compared to regular Q-learning only. Although the learning time and the weights stored are reduced, Q-learning combined with backpropagation have the same ability as regular Q-learning to play Flappy Bird game.
Hyperparameter Optimization of Random Forest for Multiclass Classification of Student Academic Performance Using Multidimensional Factors Sri Nurhayati; Diana Effendi; Bobi Kurniawan Soegoto; Adam Mukharil Bachtiar; Hanhan Maulana; Ednawati Rainarli
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.18885

Abstract

Classification for academic performances among students in a multi-class scenario is a challenging task due to its dependencies on multiple factors and characteristics, particularly in the medium academic performance category. This scenario makes it a problem for some models with their conventional settings in terms of their ability to optimally distinguish categories of academic performances while being used in classification tasks, thus leading to the need for optimization techniques in enhancing their performances. This research paper will design an optimization strategy for improving the performances of the Random Forest algorithm in a multi-class academic performance classification among students. This will help in enhancing decision-making systems in education. The research method used is a machine learning approach with a Random Forest algorithm optimized through hyperparameter tuning using RandomizedSearchCV. This study utilizes secondary student data obtained from the Kaggle public repository, consisting of 6,607 data points with 20 determining factors covering academic, behavioral, social, environmental, and health aspects. The results showed that Random Forest hyperparameter optimization was able to improve model performance from a baseline accuracy of 79.56% to 81.08% on the validation data, and achieved an accuracy of 81.69% on the test data. In addition, there was an improvement in performance in the Medium category classification, as indicated by an increase in the F1-score value from 0.69 to 0.72. Therefore, the optimization of Random Forest proved to be good in enhancing the performance and stability of multiclass classification of student academic performance.
Smart Notification System with the Integration of Robotic Process Automation and Reinforcement Learning Andri Heryandi; Sufa Atin; Hani Irmayanti; Adam Mukharil Bachtiar; Hanhan Maulana; Bobi Kurniawan Soegoto; Ednawati Rainarli
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.18951

Abstract

This study proposes the development of an intelligent academic notification system by integrating Robotic Process Automation (RPA) and Reinforcement Learning (RL) to improve the effectiveness of delivering information to students and parents. RPA is utilized to automate the process of sending notifications across various channels, such as email and WhatsApp, ensuring fast, consistent, and hands-free message distribution. RL is implemented to determine the optimal communication channel based on delivery history, message status (sent, failed, read), and the cost associated with each channel. Each student is represented as a state, while the selection of a communication channel becomes an action evaluated using Q-learning. The system learns from recipient behavior and updates the Q-table to enhance the accuracy of channel selection for future notifications. Additionally, the system applies an automatic escalation mechanism to parents as the deadline approaches. The result of this research is a smart notification system that can be implemented within academic information systems to enhance operational efficiency and student engagement.
IMPLEMENTASI METODE K-NEAREST NEIGHBOR (K-NN) DAN FORWARD CHAINING UNTUK MONITORING TUMBUH KEMBANG BALITA Petrus Sokibi Sukanto; Rifqi Fahrudin; Ridho Taufiq Subagio; Ednawati Rainarli; Adam Mukharil Bachtiar; Hanhan Maulana; Bobi Kurniawan
Jurnal Digit : Digital of Information Technology Vol 16, No 1 (2026)
Publisher : Universitas Catur Insan Cendekia (CIC) Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51920/jd.v16i1.460

Abstract

Pelayanan pelaporan hasil pemeriksaan balita di Posyandu seringkali menghadapi kendala akurasi dan keterlambatan informasi, yang menyulitkan kader serta orang tua dalam memantau tumbuh kembang anak secara efektif. Penelitian ini bertujuan untuk merancang bangun model sistem informasi berbasis website yang mampu menentukan status gizi dan perkembangan motorik balita secara akurat. Sistem ini mengintegrasikan dua metode kecerdasan buatan: K-Nearest Neighbor (K-NN) untuk klasifikasi status gizi berdasarkan antropometri, dan Forward Chaining untuk mendeteksi tahap perkembangan kemampuan motorik balita. Pengembangan model perangkat lunak dilakukan menggunakan framework CodeIgniter dengan pemodelan sistem menggunakan Unified Modelling Language (UML). Hasil penelitian menunjukkan bahwa model website ini memiliki performa yang sangat baik dengan tingkat akurasi sebesar 85,71% untuk penentuan status gizi melalui metode K-NN, dan tingkat akurasi mencapai 100% untuk identifikasi perkembangan motorik menggunakan Forward Chaining. Model ini diharapkan dapat menjadi alat monitoring yang handal bagi tenaga kesehatan dan orang tua. Sebagai pengembangan di masa depan, disarankan penambahan fitur switch akun bagi orang tua yang memiliki lebih dari satu balita untuk mempermudah manajemen data perkembangan anak secara personal.Kata kunci: Posyandu, Status Gizi, Perkembangan Balita, K-Nearest Neighbor, Forward Chaining.
Integrated and Spatiotemporal Predictive Data-Driven Narcotics Intelligence Ecosystem: Governance, Interoperability, and Analytics Pipeline for Evidence-Based Policy : Case Study: BNNP West Java, Indonesia Luki Ishwara; Agus Nursikuwagus; Ednawati Rainarli; Zainal Arifin Hasibuan; Sri Supatmi
Integrated System and Management Technology Vol. 1 No. 2 (2026): July: Integrated System and Management Technology
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ismat.v1i2.436

Abstract

Across BNN, police, health, corrections, and local government, Indonesia's crossagen cynarcotics control produces a lot of data yet fragmented, limiting timely identification of abuse patterns,hotspots, and resource requirements. It aims to bridge the gap between international breakthroughson the use of machine-learning–based monitoring and optimization under uncertainty and underdeveloped provincial integration of governance, interoperability, and predictive analytics in the public sector. It is about designing and assessing an integrated spatiotemporal predictive, datadriven narcotics intelligence ecosystem for BNNP West Java. The approach combines iterative information systems engineering with an embedded case study and a mixed-methods evaluation covering seven phases: requirements structuring; data governance and quality; federated/hybrid interoperability and Privacy-Preserving Record Linkage; spatiotemporal predictive pipelines with both baseline and advanced models and anomaly detection; hotspot and risk mapping; early warning and situational dashboards linked to operational protocols; and implementation assessment with institutional learning.Evaluation utilizes quantitative measurements for data quality and model performance (including lead time and false-alarm considerations) and qualitative findings evaluating governance readiness and usability. Expected outputs can comprise a four-pillar framework bridging governance and policy impact, replicable artefacts to be deployed at the provincial level, and implications for evidence-based narcotics policy under national digital-government agendas, with considerations for data-access andprivacy limitations.
INDEKS RISIKO BENCANA PARIWISATA DAN KLASTER KABUPATEN KOTA JAWA BARAT BERBASIS DATA WISATA 2020–2024: TOURISM DISASTER RISK INDEX AND CLUSTERING OF WEST JAVA REGENCIES AND CITIES BASED ON TOURISM DATA (2020–2024) Ucu Nugraha; Sri Titi Handayani; Hernalom Sitorus; Bobi Kurniawan S; Adam Mukharil Bachtiar; Ednawati Rainarli; Hanhan Maulana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7294

Abstract

West Java Province is one of Indonesia’s leading tourism destinations and, at the same time, a region with a high incidence of disasters. However, available disaster risk information such as the Indonesian Disaster Risk Index and the West Java Provincial Disaster Risk Assessment remains broad in scope and has not explicitly integrated the tourism dimension. This study aims to develop a Tourism Disaster Risk Index at the regency/municipality level in West Java Province by utilizing data on the number of disaster events, the number of disaster victims during the 2020–2024 period, and the number of tourism destination objects, while also clustering regions to construct a disaster-based typology of tourism risk. The methods include: (1) aggregating five-year disaster event and victim data by regency/municipality; (2) calculating the total number of tourism destination objects (natural, cultural, and man-made); (3) applying min–max normalization to produce partial indices for events, victims, and tourism destination objects; (4) constructing a composite Tourism Disaster Risk Index using weights of 0.4:0.4:0.2, in which hazard (events) and impact (victims) are deliberately assigned greater weights than tourism exposure as a conceptual decision aligned with disaster risk frameworks that prioritize life safety and physical damage; and (5) applying the K-Means algorithm (k = 3) to perform clustering based on the partial indices. The results show that the Tourism Disaster Risk Index (0–100 scale) ranges from 0.76 to 56.70, with a mean of 12.27 and a median of 6.90. A total of 25 regencies/municipalities fall into the low tourism risk category, while Bogor Regency and Cianjur Regency are in the moderate category. The clustering yields three clusters: cluster 1 comprises 21 regencies/municipalities with relatively low tourism risk; cluster 2 includes five regencies (Bogor, Bandung, Garut, Majalengka, and Pangandaran) characterized by moderate risk and a high concentration of destinations; and cluster 3 consists solely of Cianjur Regency as an outlier with exceptionally high disaster impacts. These findings provide a quantitative foundation as well as a prototype decision-support tool for local governments and stakeholders to prioritize resources and design interventions for disaster-resilient tourism development in West Java Province.  
Pelatihan Materi Berinternet Sehat Dan Bijaksana Pada Guru Madrasah Aliyah Al-Irfan Sumedang Nelly Indriani Widiastuti; Fakhrian Fadlia Adiwijaya; Ednawati Rainarli
Jurnal Pengabdian Teknik dan Ilmu Komputer (Petik) PETIK : Jurnal Pengabdian Teknik dan Ilmu Komputer Vol. 5 No. 2 Desember 2025
Publisher : Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/petik.v5i2.17099

Abstract

Kegiatan pelatihan kepada guru-guru madrasalah Aliyah Al-Irfan bertujuan untuk  memberikan pengetahuan dan kesadaran terhadap dampak yang muncul dari penggunaan internet dan media sosial. Terutama dampak negatif seperti hoax, cyberbullying, atau penipuan. Generasi Z adalah remaja yang termasuk banyak mengakses internet terutama sosial media. Hal ini menjadi salah satu sebab mereka memiliki potensi terkena dampak negatif. Usia tersebut sesuai dengan siswa madrasah al-Irfan yang harus dihadapi oleh guru-guru peserta Pengabdian pada Masyarakat ini.  Guru-guru memiliki peran yang besar dalam mendidik pada siswanya agar mampu menghadapi dampak negatif yang  ditimbulkan penggunaan internet. Kegiatan dilaksanakan dengan metode ceramah pemberian materi kemudian dilanjutkan dengan diskusi atau sharing pengalaman dan tanya jawab. Evaluasi dilakukan dengan memberikan beberapa soal pada prates dan paskates. Pertanyaan disusun berdasarkan materi yang diberikan. Hasil evaluasi tersebut menunjukkan bahwa perserta secara umum telah mengetahui dampak yang mungkin dialami oleh siswa mereka. Untuk selanjutnya yayasan dan peserta merasa perlu ditindaklanjuti kegiatan ini dengan materi yang disesuaikan dengan kebutuhan
Integrasi Gemini AI Berbasis Retrieval-Augmented Generation (RAG) pada Moodle untuk Penilaian Esai Otomatis dengan Pendekatan Human-in-the-Loop di Pendidikan Tinggi Rizki Adha; Lusianto Lusianto; Dodi Syaripudin; Bobi Kurniawan S; Adam Mukharil Bachtiar; Hanhan Maulana; Ednawati Rainarli
Academic Journal of Computer Science Research Vol 8, No 1 (2026): Academic Journal of Computer Science Research (AJCSR)
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/ajcsr.v8i1.16253

Abstract

Penilaian esai merupakan komponen penting dalam evaluasi pembelajaran di pendidikan tinggi, namun proses penilaian manual oleh dosen membutuhkan waktu yang besar dan berpotensi menimbulkan inkonsistensi, terutama pada kelas dengan jumlah mahasiswa yang besar. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi sistem penilaian esai otomatis berbasis Gemini AI yang terintegrasi dengan Learning Management System (LMS) Moodle menggunakan pendekatan Retrieval-Augmented Generation (RAG) dan mekanisme Human-in-the-Loop (HIL). Penelitian menggunakan metode Research and Development (R&D) dengan model ADDEI, yang meliputi tahap analisis, perancangan, pengembangan, evaluasi, dan implementasi sistem. Evaluasi sistem dilakukan melalui pengujian fungsional (black-box testing), serta kuesioner Human-in-the-Loop yang melibatkan 24 dosen dari 14 perguruan tinggi swasta di wilayah Banten, DKI Jakarta, dan Jawa Barat. Hasil pengujian menunjukkan bahwa seluruh fungsi sistem berjalan sesuai dengan spesifikasi. Evaluasi HIL menunjukkan tingkat penerimaan yang tinggi hingga sangat tinggi, terutama pada indikator peran AI sebagai decision-support system dan tanggung jawab akademik dosen. Selain itu, hasil validasi dosen menunjukkan bahwa sebagian besar rekomendasi skor dan umpan balik yang dihasilkan oleh sistem dapat diterima, dengan dosen tetap memiliki kendali penuh dalam menentukan nilai akhir. Temuan ini menunjukkan bahwa integrasi Gemini AI berbasis RAG dengan mekanisme Human-in-the-Loop efektif sebagai sistem pendukung penilaian esai yang efisien, akuntabel, dan sesuai dengan kebutuhan penilaian akademik di pendidikan tinggi.
Computer Vision Analysis for Traffic Monitoring and Road Safety in Smart City Concept Luki Ishwara; Hasbu Naim Syaddad; Andi Agus Salim; Bobi Kurniawan; Adam Mukharil Bachtiar; Ednawati Rainarli
Technologia Journal Vol. 3 No. 1 (2026): Technologia Journal-February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/vk562576

Abstract

Rapid urban growth and rising traffic complexity require Smart City solutions that move beyond passive CCTV toward intelligent, real-time traffic management. This study examines how computer vision–based analytics contribute to road safety when integrated into an Intelligent Transportation System (ITS). A quantitative quasi-experimental design was applied across multiple intersections using a 12-month before–after window. Data were collected from video analytics (vehicle and pedestrian detection, tracking, violations, road conditions), adaptive signal logs, crash and injury records, near-miss indicators, and contextual variables such as weather and traffic volume. Analysis combined perception validation (mAP, tracking accuracy), time-series operational assessment, and Difference-in-Differences modeling to estimate safety impacts. Results show high perception reliability (mAP > 0.85) and significant operational improvements, including a 33% reduction in waiting time and 35% shorter queues. More importantly, red-light violations decreased by 39%, near-miss events by 45%, crash frequency by 42%, and severity index by 37%. The findings indicate a causal pathway from vision-based perception to adaptive control and enforcement, leading to measurable safety gains. The study concludes that computer vision serves as a safety governance instrument within Smart City ITS when detection outputs are tightly coupled with intervention mechanisms.   
Co-Authors Adam Mukharil Bachtiar Agus Nursikuwagus Andi Agus Salim Andri Heryandi Ardiansyah Arif Romadhan Bobi Kurniawan S Bobi Kurniawan Soegoto Bobi Kurniawan, Bobi Diana Effendi Dodi Syaripudin Fakhrian Fadlia Adiwijaya Hani Irmayanti Hernalom Sitorus Luki Ishwara Lusianto Lusianto Maulana, Hanhan Nelly Indriani Widiastuti Petrus Sokibi Sukanto Ridho Taufiq Subagio Rifqi Fahrudin Rizki Adha Sri Nurhayati Sri Supatmi Sri Titi Handayani Sufa Atin Syaddad, Hasbu Naim Ucu Nugraha Zainal Arifin Hasibuan