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All Journal IPTEK The Journal for Technology and Science JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal EECCIS Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIN (Jurnal Online Informatika) JIEET (Journal of Information Engineering and Educational Technology) Building of Informatics, Technology and Science Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Eduvest - Journal of Universal Studies Sewagati Jurnal Ilmiah Multidisiplin Indonesia
Shintami Chusnul Hidayati
Institut Teknologi Sepuluh Nopember
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Dentistry Earth & Planetary Sciences Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Stimulasi Berpikir Komputasi Melalui Digital Storytelling Menggunakan CoSpaces Edu Yeni Anistyasari; Ekohariadi Ekohariadi; Shintami C Hidayati
JIEET (Journal of Information Engineering and Educational Technology) Vol. 6 No. 1 (2022)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v6n1.p1-6

Abstract

Digital storytelling memberikan pendekatan pedagogis untuk mendorong mahasiswa berpikir algoritmik dan keterampilan mendongeng untuk membuat cerita yang bermakna dan relevan dengan kebutuhan dan minat pribadi. Penelitian ini memanfaatkan CoSpaces Edu berbasis animasi 3D sebagai perangkat untuk menciptakan digital storytelling. Jumlah responden yang digunakan yaitu 120 mahasiswa non-teknik di tahun pertama. Mahasiswa diminta membuat digital storytelling kemudian pengajar menilainya berdasarkan rubrik penilaian yang tersedia. Dimensi berpikir komputasi terdiri dari Abstraction, Paralellism, Logical Thinking, Synchronization, Flow Control, User Interactivity, dan Data Representation. Sedangkan dimensi digital storytelling yaitu Ide, Pengorganisasian, Voice & Tone, Pemilihan kata, Penyusunan kalimat, dan Convention. Data yang terkumpul dianalisis menggunakan SEM-PLS. Hasil model pengukuran memenuhi persyaratan seperti reliabilitas, validitas konvergen, dan diskriminan. Begitu pula dengan hasil struktural menunjukkan bahwa digital storytelling berpengaruh signifikan positif terhadap berpikir komputasi.
Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose Dava Aulia; Riyanarto Sarno; Shintami Chusnul Hidayati; Alfian Nur Rosyid; Muhammad Rivai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp264-275

Abstract

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.
Optimizing Segmentation and Purchase Forecasting in Credit Card Transactions: A PSO-enhanced k-means and ANN Approach Hidayati, S.Kom., M.Sc., Ph.D., Shintami Chusnul; Raharja, Putu Bagus Gede Prasetyo; Wardhiana, I Nyoman Gde Artadana Mahaputra; Klemm, Sebastian
JIEET (Journal of Information Engineering and Educational Technology) Vol. 7 No. 2 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v7n2.p59-65

Abstract

In the rapidly evolving landscape of data-driven marketing, machine learning has emerged as a pivotal tool for analyzing complex consumer behaviors and enhancing strategic decision-making. This paper introduces a novel approach to optimize customer segmentation and purchase forecasting in credit card transactions through the synergistic integration of Particle Swarm Optimization (PSO)-enhanced k-means clustering and Artificial Neural Networks (ANN). The proposed methodology refines customer segmentation by leveraging PSO, resulting in more defined clusters. In the predictive modeling phase, an ANN outperforms conventional methods, providing superior accuracy in purchase forecasting. The study demonstrates the effectiveness of advanced algorithms in enhancing insights from credit card transaction data, offering valuable implications for improved decision-making in the financial domain.
Pelatihan Desain Antarmuka Mobile Application dengan Figma untuk Meningkatkan Kompetensi Guru MGMP TIK Surabaya Ahmadiyah, Adhatus Solichah; Sarno, Riyanarto; Hidayati, Shintami Chusnul; Anggraini, Ratih Nur Esti; Sungkono, Kelly Rossa; Munif, Abdul
Sewagati Vol 8 No 4 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i4.1216

Abstract

Studi ini mengevaluasi dampak pelatihan desain antarmuka mobile application (aplikasi perangkat bergerak) menggunakan Figma terhadap peningkatan wawasan dan keterampilan guru MGMP TIK di Surabaya. Kegiatan pelatihan yang diikuti 43 peserta terlaksana dalam dua tahap, yakni penyampaian materi dan pendampingan berupa asistensi pengerjaan desain antarmuka pada studi kasus. Metode evaluasi melibatkan pengumpulan data melalui kuesioner yang diberikan sebelum dan setelah pelatihan, serta analisis hasil penugasan desain antarmuka pada studi kasus. Sebagaimana terlihat dari hasil kuesioner. Hasil karya desain antarmuka peserta juga mencerminkan penguasaan konsep desain antarmuka aplikasi perangkat bergerak. Implikasi dari peningkatan ini berupa kontribusi positif terhadap pendekatan pembelajaran berbasis teknologi di lingkungan sekolah. Kesimpulannya, pelatihan desain antarmuka aplikasi perangkat bergerak menggunakan Figma mampu secara efektif meningkatkan pengetahuan dan keterampilan guru MGMP TIK Surabaya, yang selanjutnya dapat diterapkan dan dioptimalkan dalam konteks pembelajaran teknologi di sekolah. Studi ini memberikan landasan bagi pengembangan lebih lanjut dalam memperkaya metode pelatihan guru untuk menghadapi tantangan pembelajaran berbasis teknologi di era digital.
Algorithmic Advancements in Heuristic Search for Enhanced Sudoku Puzzle Solving Across Difficulty Levels Pratama, Moch Deny; Abdillah, Rifqi; Herumurti, Darlis; Hidayati, Shintami Chusnul
Building of Informatics, Technology and Science (BITS) Vol 5 No 4 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.4622

Abstract

Computer technology, particularly artificial intelligence, has found diverse applications in the rapidly evolving era of the industrial revolution, notably in gaming, delving into artificial intelligence and explicitly applying game-solving techniques to Sudoku puzzles. Sudoku, a popular game requiring logical precision, serves as an ideal platform for exploring algorithms such as depth-first search, breadth-first search, and heuristic search. This research identifies memory-intensive demands in breadth-first search and the potential issue of infinite traversal in depth-first search. To address these challenges, the study proposes implementing the heuristic search algorithm, which prioritizes promising paths based on estimations of proximity to the goal state made by a heuristic function. The primary objective is to enhance Sudoku puzzle-solving by comparing the performance of the heuristic search algorithm with traditional breadth-first and depth-first search methods, with a particular focus on improving efficiency and reducing memory usage, including time and steps. The results indicate that the heuristic search algorithm outperforms traditional methods, demonstrating faster completion times and reduced memory requirements, thereby contributing to the advancement of Sudoku-solving algorithms. The study evaluates their performance across different difficulty levels, utilizing data from sudoku.com and extremesudoku.info. Notably, the heuristic search algorithm emerges as a superior method, outperforming other algorithms in terms of completion steps and time efficiency. The implementation and analysis involved three types of Sudoku puzzle-solving methods, revealing that the heuristic search algorithm significantly outperforms other algorithms, optimizing its performance in solving Sudoku puzzles. The average time required to complete Sudoku puzzles from data sourced from Sudoku.com was 0.02, 0.05, and 0.61 seconds for each level, respectively. In contrast, according to extremesudoku.info, it took 0.31 seconds for the highest difficulty level. Furthermore, the average total steps needed on sudoku.com ranged from 43 to 1201 steps for each level, spanning from easy to hard. On extremesudoku.info, 509 steps were required for the highest difficulty level. These results affirm the reliability of heuristic search, consistently demonstrating encouraging outcomes and outperforming other algorithms across diverse conditions. This strategic selection facilitates a comprehensive analysis of Sudoku problem-solving algorithms, allowing for the exploration of algorithmic performance and providing a comprehensive range of Sudoku puzzles, thereby ensuring the study's robustness and validity
Stacking-based ensemble learning for identifying artist signatures on paintings Hidayati, Shintami Chusnul; Irawan Rahardja, Agustinus Aldi; Suciati, Nanik
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1683-1693

Abstract

Identifying artist signatures on paintings is essential for authenticating artworks and advancing digital humanities. An artist’s signature is a consistent element included in each painting that the artist creates, providing a unique identifier for their work. Traditional methods that rely on expert analysis and manual comparison are time-consuming and are prone to human error. Although convolutional neural networks (CNNs) have shown promise in automating this process, existing single-model approaches struggle with the diversity and complexity of artistic styles, leading to limitations in their performance and generalizability. Therefore, this study proposes an ensemble learning approach that integrates the predictive power of multiple CNN-based models. The proposed framework leverages the strengths of three state-of-the-art CNNs: EfficientNetB4, ResNet-50, and Xception. These models were independently trained, and the predictions were combined using a meta-learning strategy. To address class imbalance, data augmentation techniques and weighted loss functions were employed. The experimental results obtained on a dataset of more than 8,000 paintings from 50 artists demonstrate significant improvements over individual CNN architectures and other ensemble methods, thereby effectively capturing complex features and improving generalizability.
Evaluasi Capstone Project Desain Antarmuka Aplikasi untuk Guru MGMP TIK Surabaya Adhatus Solichah Ahmadiyah; Riyanarto Sarno; Shintami Chusnul Hidayati; Ratih Nur Esti Anggraini; Kelly Rosa Sungkono; Abdul Munif
ABDI: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 6 No 3 (2024): Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : Labor Jurusan Sosiologi, Fakultas Ilmu Sosial, Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/abdi.v6i3.905

Abstract

Peningkatan keterampilan desain aplikasi bagi guru MGMP TIK Surabaya penting untuk ditingkatkan guna mendukung kualitas pendidikan yang lebih baik. Kegiatan pengabdian kepada masyarakat ini dilakukan oleh tim dosen dan mahasiswa laboratorium manajemen cerdas informasi teknik informatika ITS dengan tujuan memberikan rekomendasi dan masukan terkait permasalahan desain antarmuka aplikasi yang dibuat oleh guru peserta pelatihan. Metode yang digunakan adalah evaluasi heuristik dan klasifikasi permasalahan menggunakan severity level. Hasil dari kegiatan ini menunjukkan bahwa guru menjadi tahu apa permasalahan yang ada di desain antarmuka yang dibuat dan mengetahui aksi perbaikan. Hal ini juga memperjelas penyampaian materi dengan permasalahan dan solusi nyata, sehingga dapat meningkatkan kualitas pembelajaran dan pengajaran
Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance Sjahrunnisa, Anita; Suciati, Nanik; Hidayati, Shintami Chusnul
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 18 No. 2 (2024)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v18i2.1707

Abstract

Stock price prediction continues to be a major focus for investors today, some previous studies often focus on technical analysis using historical stock price data and ignore external factors that can affect stock prices. The purpose of this research is to overcome the shortcomings of previous research by creating a stock price prediction model that combines historical stock data consisting of date, high, low, open, close, adj close, volume and external factors such as days, interest rates, inflation, and dividends. The data used came from 33 companies from 11 industrial sectors in Indonesia for 2267 trading days and evaluated the prediction performance using MSE, MAPE and R-squared. The results show a significant improvement in the evaluation metrics when external factors are added. This shows the importance of such factors in improving the prediction analysis and increasing the reliability of the prediction model. This approach is expected to not only overcome the limitations of traditional methods but also utilize a combination of deep learning and machine learning to improve prediction accuracy. Thus, this research not only provides new insights in the field of financial analysis but also provides new insights and solutions for investors to make more informed and less risky decisions.
Road Damage Detection Using YOLOv7 with Cluster Weighted Distance-IoU NMS Rachman, Rudy; Suciati, Nanik; Hidayati, Shintami Chusnul
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1481

Abstract

Road damage can occur everywhere. Potholes are one of the most common types of road damage. Previous research that used images as input for pothole detection used the Faster Regional Convolutional Neural Network (R-CNN) method. It has a large inference time because it is a two-stage detection method. The object detection method requires post-processing for its detection results to save only the best prediction from the method, namely, non-maximum suppression (NMS). However, the original NMS could not properly detect small, far, and two objects close to each other. Therefore, this research uses the YoloV7 method as the object detection method because it has better mean Average Precision (mAP) results and a lower inference time than other object detection methods; with an improved NMS method, namely Cluster Weighted Distance Intersection over Union (DIoU) NMS (CWD-NMS), to solve small or close potholes. When training YoloV7, we combined a new, independently collected pothole dataset, with previous public research datasets, where the detection results of the YoloV7 method were better than those of Faster R-CNN. The YoloV7 method was trained using various scenarios. The best scenario during training is using the best checkpoint without using a scheduler. The mAP.5 and mAP.5-.95 value of CWD-NMS was 89.20% and 63.30% with 10.30 millisecond per image for inference time.
Analysis of Taxpayer Behavior to Predict Motor Vehicle Tax Payments Using the Weighted Majority Voting Ensemble Approach Wahyuwidayat, Raditia; Saikhu, Ahmad; Hidayati, Shintami Chusnul
IPTEK The Journal for Technology and Science Vol 35, No 2 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i2.19196

Abstract

Taxpayer non-compliant behavior impacts Motor Vehicle Tax (MVT) revenues not following the predetermined targets. This behavior results in reduced income, and several regional development targets may not be achieved. Therefore, Regional Governments need to predict MVT payments to formulate future targets better. This research aims to analyze taxpayer behavior in predicting future MVT payments, whether the payments are compliant or late or non-payment. The proposed approach starts by analyzing and obtaining a dataset of taxpayer behavioral features. An ensemble classifier method based on Weighted Majority Voting (WMV) is used to predict payments. WMV was developed using the GridSearchCV technique to find optimal hyperparameter values to increase the model accuracy value for individual classifiers. The weight determined from the model accuracy value is converted into a ranking of the number of votes to maximize model performance. Next, feature ablation analysis is carried out to understand the contribution of each feature to model performance. The performance of the proposed system is evaluated using the confusion matrix, accuracy, precision, recall, and f1-score. The research results show that the WMV method performs better, with an accuracy of 96.247%, compared to the proposed individual classifier method in predicting MVT payments based on taxpayer behavior.
Co-Authors Abdillah, Rifqi ABDUL MUNIF ABDUL MUNIF Adhatus Solichah Ahmadiyah Adhatus Solichah Ahmadiyah, Adhatus Solichah Ahmadiyah, Adhatus Solichah Baskoro Adi Pratomo Darlis Herumurti Dava Aulia Ekohariadi Irawan Rahardja, Agustinus Aldi Jaya, Muhammad Triyanda Taruna Kelly Rosa Sungkono Kelly Rossa Sungkono Klemm, Sebastian Mohamad Syazimmi Hersyaputra Muhamad Hilmil Muchtar Aditya Pradana Muhammad Rivai Nafa Zulfa Nanik Suciati Navinda Meutia Nurul Fajrin Ariyani Pratama, Moch Deny Rachman, Rudy Raharja, Putu Bagus Gede Prasetyo Ratih Nur Esti Anggraini Ratih Nur Esti Anggraini, Ratih Nur Esti Ravi Vendra Rishika Riyanarto Sarno Rosyid, Alfian Nur Saikhu, Ahmad Sjahrunnisa, Anita Sulaeman Salasa Wahyuwidayat, Raditia Wardhiana, I Nyoman Gde Artadana Mahaputra Yeni Anistyasari