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All Journal Infotech Journal JITEK (Jurnal Ilmiah Teknosains) Format : Jurnal Imiah Teknik Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) JIEET (Journal of Information Engineering and Educational Technology) InComTech: Jurnal Telekomunikasi dan Komputer Jurnal ULTIMATICS Jurnal ULTIMA Computing JURIKOM (Jurnal Riset Komputer) KOMPUTEK Majalah Ilmiah UNIKOM Jurnal Informatika dan Rekayasa Perangkat Lunak JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Infortech Djtechno: Jurnal Teknologi Informasi Computer Science (CO-SCIENCE) IMTechno: Journal of Industrial Management and Technology Simpatik: Jurnal sistem Informasi dan Informatika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Artificial Intelligence and Digital Business International Journal of Advanced Science Computing and Engineering Journal Medical Informatics Technology Jurnal Informatika: Jurnal Pengembangan IT Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Waeisul Bismi
Universitas Bina Sarana Informatika
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Pengembangan Aplikasi Web untuk Resize Citra Digital dengan Fitur Batch Processing Menggunakan Next.Js dan Sharp Waeisul Bismi; Muhammad Qomaruddin; Nila Hardi; Musriatun Napiah; Astrid Noviriandini
KOMPUTEK Vol. 10 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Ponorogo

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Abstract

The exponential growth of digital content has increased the demand for efficient and accessible image processing tools. This research aims to develop a web-based image resize application with batch processing features using Next.js and Sharp. The research method employs Research and Development (R&D) with a Software Development Life Cycle (SDLC) approach using the Waterfall model, encompassing requirements analysis, system design, implementation, testing, deployment, and maintenance phases. The application was developed by integrating Next.js 16 framework for full-stack development, Sharp library for high-performance image processing, and JSZip for archive handling. Implemented features include flexible upload (file, folder, ZIP), downsampling and upsampling options, pixel dimension input, JPEG/JPG/PNG format conversion, and batch processing with progress monitoring. Testing results demonstrated that 100% of features were successfully implemented with a functional testing success rate of 100%. The average response time achieved 1.76 seconds per image, 41% faster than the 3-second target. The quality of the test results shows that the quality of the resized images meets very good quality standards with high structural similarity to the original images for both downsampling and upsampling. This research has produced a web application for image resizing that is accessible without installation, efficient for batch processing, and produces optimal output quality by utilizing the Mitchell interpolation kernel for downsampling and Lanczos for upsampling
The Implementation Of Agile Methods In Designing A Web-Based Information System In The Geomin Laboratory Of Pt Antam Tbk Dennis Bramastha; Waeisul Bismi
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4426

Abstract

The management of human resource administration at the Geomin Laboratory Unit of PT Antam Tbk, which includes recording attendance, overtime requests, and leave requests, faces various obstacles due to the continued implementation of manual procedures. This process, which relies on physical forms, is not only time-consuming in recapitulation, but is also prone to recording errors and document loss, as well as the lack of accurate quota monitoring. This study aims to design and build a web-based information system that can overcome these problems by integrating all administrative processes into a single digital platform. The development method used in this study is the Agile method, which allows for a flexible and iterative development process. This system is built using the Laravel framework and utilizes geolocation technology for attendance validation. The result of this study is a functional information system with key features such as dare attendance, a multi-level approval flow, and real-time leave quota management. Based on the results of the User Acceptance Test involving 15 respondents from various roles, this system achieved an acceptance rate of 93.1%, which falls into the “Highly Acceptable” category.
ANALISIS SENTIMEN ULASAN APLIKASI PORTAL PULSA PADA GOOGLE PLAY STORE MENGGUNAKAN METODE MACHINE LEARNING Zalukhu, Sampril Yanus; Bismi, Waeisul; Agustiani, Sarifah
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 10 No. 2 (2026): Artificial Intelligence (AI)
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v10i2.1296

Abstract

Portal Pulsa application is a pulse refilling and bill payment service platform that has a high number of reviews on the Google Play Store. These reviews can be used to determine user perception of application services. This study aims to perform sentiment analysis on Portal Pulsa application user reviews using machine learning methods. The research stages include collecting review data from the Google Play Store, text preprocessing, feature extraction using TF-IDF, and sentiment classification using several machine learning algorithms, namely Naïve Bayes, Linear Support Vector Machine (Linear SVM), Random Forest, K-Nearest Neighbor (KNN), and Decision Tree. Model evaluation was performed using accuracy, precision, recall, and F1-score metrics. The results showed that the Linear SVM algorithm provided the best performance with an accuracy value of 93.24%. These results indicate that Linear SVM is effectively used in classifying the sentiment of Portal Pulsa application user reviews.
Comparative Performance Analysis of ML, DL, and Transformer Models for Sentiment Classification of Indonesian Mobile Banking User Reviews Waeisul Bismi; Muhammmad Qomaruddin; Siti Marlina
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

The rapid development of digital technology has encouraged the adoption of mobile banking applications in Indonesia, but it has also led to an increase in user complaints and reviews regarding performance and ease of use. This study aims to conduct a comparative analysis of the performance of Machine Learning, Deep Learning, and Transformer (IndoBERT) models in classifying the sentiment of user reviews of Indonesian-language mobile banking applications. Data was collected through web scraping from the Google Play Store on ten leading banking applications in Indonesia with a total of 200,000 reviews. After going through the preprocessing stages of cleaning, normalisation, tokenisation, and stemming, automatic labelling was carried out based on ratings into three sentiment classes: positive, neutral, and negative. Machine learning models (Naïve Bayes, Logistic Regression, Random Forest, and SVM) were built using TF-IDF feature representation, while deep learning models (LSTM, Bi-LSTM, GRU, and CNN) utilised 128-dimensional word embeddings. The Transformer-based IndoBERT model was fine-tuned with a sequence classification configuration. The evaluation used accuracy, precision, recall, and weighted F1-score metrics, accompanied by an analysis of training and testing time efficiency. The results show that the Bi-LSTM model performs best with an accuracy of 83.47% and an F1-score of 80.78%, followed by CNN (83.11%) and SVM (82.85%), while IndoBERT records an accuracy of 81.73% with a precision of 76.96%. In terms of efficiency, Logistic Regression showed an optimal balance between accuracy and training time (27.7 seconds), while deep learning and transformer models required higher computational resources. This study emphasises the importance of model selection based on requirements, between maximum accuracy and computational efficiency, and enriches the literature on Indonesian sentiment analysis in the domain of digital financial services.
ANALISIS SENTIMEN MASYARAKAT TERHADAP PENGGUNAAN GEMINI AI DENGAN METODE MACHINE LEARNING Rivana Rosandi; Ade Ilham Febrianto; Afrizal Achmad Gibran; Waeisul Bismi; Ika Kurniawati; Riza Fahlapi
Djtechno: Jurnal Teknologi Informasi Vol 6, No 3 (2025): Desember
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i3.7962

Abstract

Meningkatnya popularitas Gemini AI sebagai platform percakapan digital besutan Google mendorong perlunya memahami bagaimana masyarakat Indonesia menilai kehadirannya. Namun, kajian mengenai persepsi publik berbasis data empiris dalam konteks layanan AI generatif masih terbatas. Penelitian ini bertujuan mengisi kesenjangan tersebut dengan menganalisis sentimen pengguna terhadap Gemini AI menggunakan 10.000 ulasan dari Google Play Store. Data diolah melalui tahapan praproses teks dan pelabelan sentimen, kemudian diklasifikasikan menggunakan beberapa model machine learning untuk memperoleh gambaran yang lebih komprehensif. Hasil penelitian menunjukkan bahwa model SVM memberikan performa paling unggul sebesar 96,34%, precision 0,97%, recall 0,95%, dan F1-score 0,96% mengungguli secara signifikan Naive Bayes (94,76%), Logistic Regression (94,24%), dan Random Forest (93,19%) dan mengindikasikan kecenderungan sentimen positif masyarakat terhadap Gemini AI. Temuan ini memberikan gambaran awal bagi pengembang untuk meningkatkan kualitas layanan dan pengalaman pengguna secara berkelanjutan, khususnya dalam menghadapi persaingan teknologi AI yang semakin dinamis.
ANALISIS SENTIMEN ULASAN APLIKASI X PADA GOOGLE PLAY STORE MENGGUNAKAN KOMPARASI SVM, KNN, DAN NAIVE BAYES Jim Maxwell Manampiring; Jenianus Halawa; Jefiri Zai; Ika Kurniawati; Riza Fahlapi; Waeisul Bismi
Djtechno: Jurnal Teknologi Informasi Vol 7, No 1 (2026): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i1.8095

Abstract

Transformasi rebranding media sosial Twitter menjadi X menimbulkan respons yang beragam dari pengguna global, termasuk di Indonesia. Ulasan pengguna pada platform Google Play Store memuat informasi berharga mengenai kepuasan dan keluhan pengguna, namun volume data yang besar dan tidak terstruktur menyulitkan analisis secara manual. Studi ini difokuskan pada penerapan analisis sentimen guna mengklasifikasikan opini pengguna menjadi kategori positif dan negatif, serta membandingkan kinerja tiga algoritma Machine Learning, yaitu Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan Naive Bayes. Dataset yang digunakan berjumlah 10.000 data berbahasa Indonesia yang dikumpulkan melalui scraping. Melalui tahapan preprocessing yang meliputi cleaning, tokenizing, dan stemming, data dilatih dengan pembagian rasio 80:20. Hasil pengujian menunjukkan algoritma SVM menggunakan kernel linear menghasilkan kinerja terbaik dengan akurasi sebesar 84,8%, diikuti oleh Naive Bayes sebesar 83,1%, dan KNN sebesar 79,7%. Kesimpulan dari studi ini menegaskan bahwa SVM merupakan metode yang paling efisien guna menangani klasifikasi teks pada data ulasan aplikasi X yang memiliki dimensi tinggi, meskipun terdapat ketidakseimbangan kelas pada dataset.
Implementasi YOLOv8 dan FaceNet untuk Sistem Keamanan Real-Time Berbasis IoT Ery Kurniawan; Rifqi Rahmandhani; Dimas Rizkiansyah; Ika Kurniawati; Waeisul Bismi; Riza Fahlapi
FORMAT Vol 15 No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2026.v15.i1.009

Abstract

Sistem keamanan CCTV konvensional umumnya hanya berfungsi sebagai perekam pasif tanpa kemampuan analisis otomatis, yang menyebabkan keterlambatan deteksi karena proses identifikasi dilakukan secara manual. Keterbatasan ini menimbulkan latensi tinggi dan akurasi deteksi yang rendah, sehingga menjadi masalah krusial dalam kebutuhan keamanan modern. System keamanan yang baik dapat mencegah tindak kejahatan yang bisa merugikan penghuni rumah baik fisik maupun materiil. Penelitian ini mengusulkan pengembangan sistem keamanan cerdas berbasis Internet Of Things (IoT) dengan integrasi deteksi wajah menggunakan YOLOv8 dan pengenalan wajah FaceNet menggunakan modul ESP32-CAM. Sistem ini dirancang untuk mendeteksi wajah secara real-time, identifikasi individu secara otomatis, serta pengiriman notifikasi instan melalui Telegram ketika terdeteksi wajah yang tidak dikenal. Metode penelitian ini meliputi perancangan arsitektur IoT, pengambilan dataset wajah, preprocessing menggunakan MTCNN, FaceNet untuk menghasilkan facial embeddings, serta implementasi YOLOv8 sebagai detektor wajah real-time. Evaluasi kinerja pengenalan wajah dilakukan dengan menerapkan metode 5-fold cross-validation pada dataset embedding FaceNet menggunakan pengklasifikasi k-NN. Hasil eksperimen menunjukan bahwa sistem mampu mendeteksi wajah dengan tingkat respon tinggi dan mengenali individu dengan akurasi yang konsisten pada pencahayaan dan jarak bervariasi. Hasil pengujian training rata-rata accuracy Top-1 mencapai 0.96 dan rata-rata accuracy Top-5 sebesar 0.99, YOLOv8 menunjukkan kemampuan deteksi wajah yang akurat dan cepat dengan waktu respon 1,86 detik pada server berbasis CPU Intel Core i5 dan GPU Intel UHD Graphics 620. Performa pengujian akurasi FaceNet dengan pengklasifikasi k-NN menghasilkan akurasi 99.35%, presisi 99,35%, recall 98,94%, F1-score 99,11%, dan FPR (False Positive Rate) 0,08%, hal ini menunjukkan bahwa sistem memiliki akurasi pengenalan wajah yang sangat tinggi dan konsisten. Sistem yang dikembangkan mampu memberikan peringatan instan kepada pengguna melalui Telegram saat terdeteksi wajah yang tidak dikenal, sehingga meningkatkan waktu respons terhadap potensi ancaman. Dengan performa yang stabil dan tangguh serta biaya implementasi yang rendah, sistem ini menawarkan solusi keamanan modern yang lebih adaptif, proaktif, efektif, dan efisien dibandingkan CCTV konvensional.
Co-Authors Abdullah, Fikrian Nur Ade Ilham Febrianto Ade Setiawan Ade Setiawan Afrizal Achmad Gibran Agustiani, Sarifah Anisa Febriyani Anton . Arina Selawati Astrid Noviriandini Bela, Sintia Dava Al Riziq Dennis Bramastha Deny Novianti Dhiaulhaq Ramadhan Dicky Hariyanto Dimas Rizkiansyah Dwiza Riana Dwiza Riana Ery Kurniawan Fadhil Marzuqi Fahlapi, Riza Fajar Shidiq Farid Ramadhan Febriyani, Anisa Firmansyah Firmansyah Gata, Windu Hani Harafani Harianja, Putri Alletheia Hewiz , Alya Shafira Hildan Zafa Riyadi Ika Kurniawati Ika Kurniawati Ika Kurniawati Ince Olviana Inya Mete Jefiri Zai Jenianus Halawa Jessica Tedja Jim Maxwell Manampiring Jordy Lasmana Putra Jufriadif Na`am, Jufriadif Kurniawan, Ery Liya, Amel Maysaroh, Maysaroh Mugi Raharjo Muhamad Abdul Salam Muhammad Qomaruddin muhammad qomaruddin Muhammmad Qomaruddin Musriatun Napiah Nila Hardi Noor, Mohamad Nurardian, Ridwana Septian Nurbaety Nurbaety Putra Muamar Kadafi Putri, Destiana Putri, Halimmatussa’diyah Putri, Halimmatussa’diyah Qomaruddin, Muhammmad Rachmat Adi Purnama Rachmawati Darma Astuti Rachmawati Darma Astuti Raharjo, Mugi Rahmandhani, Rifqi Raka Satria Gumilang Raya Ramadhan, Farid Rangga Wardhana Rifqi Rahmandhani Rivana Rosandi RIZA FAHLAPI Rizal Fahlapi Rizkiansyah, Dimas Sakata, Yama Sulkhan Sandra Dewi Saraswati Saputra, Ari Setia Saputra, Atio Wahyudi Siregar, Aris Parnius Siti Marlina Taufik Asra Thalut Syaputra Tommi Alfian Armawan Sandi Vannes Wijaya, Aryanata Yerico Purba Zalukhu, Sampril Yanus