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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) snimed Edutech: Jurnal Teknologi Pendidikan Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Format : Jurnal Imiah Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Pengabdian Masyarakat AbdiMas Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Petir Aptisi Transactions on Management CSRID (Computer Science Research and Its Development Journal) CCIT (Creative Communication and Innovative Technology) Journal JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Abdimas Universal Informatika TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri ICIT (Innovative Creative and Information Technology) Journal Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Sistem Komputer & Kecerdasan Buatan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat JUKI : Jurnal Komputer dan Informatika Jurnal Abdidas Jurnal Teknik Informatika (JUTIF) International Journal Of Science, Technology & Management (IJSTM) Entrepreneurship Bisnis Manajemen Akuntansi (E-BISMA) International Journal of Engineering, Science and Information Technology Jurnal Abdimas Bina Bangsa Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta Jurnal Minfo Polgan (JMP) Formosa Journal of Science and Technology (FJST) Malcom: Indonesian Journal of Machine Learning and Computer Science Universal Raharja Community (URNITY Journal) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Kohesi: Jurnal Sains dan Teknologi Journal of Informatics and Information Security International Journal of Educational Research Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Diah Aryani, Diah
Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Esa Unggul
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Pelatihan dan Implementasi Sistem Verifikasi Ijazah Digital Berbasis n8n dan Filament di SMKN 1 Kabupaten Tangerang Asri, Jefry Sunupurwa; Aryani, Diah; Fannya, Puteri; Arfian, Muhamad Hadi; Widyawan, Tri Ismardiko; Ramadhan, Iksan
Abdimas Universal Vol. 8 No. 1 (2026): April
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Balikpapan (LPPM UNIBA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36277/abdimasuniversal.v8i1.2838

Abstract

This community service activity was conducted to address the problem of diploma verification at SMKN 1 Kabupaten Tangerang, which is still carried out manually, requiring considerable time and posing risks of recording errors and document loss. This activity aims to develop and implement a digital-based diploma verification system to improve efficiency, accuracy, and the quality of administrative services. The system utilizes Laravel Filament as the administrative interface, n8n for workflow automation, MySQL as the database, and Optical Character Recognition (OCR) technology to automatically extract information from diploma documents. The implementation results show a significant performance improvement, where verification time decreased by more than 90%, from 10–15 minutes to less than one minute per document. In addition, data accuracy increased from approximately 85% to 98%, and document retrieval speed improved by more than 80%. Based on user satisfaction questionnaire results, 100% of respondents stated that the system is easy to use and helps accelerate administrative tasks. Therefore, the developed system is proven to be effective in supporting the digital transformation of academic document management in the school environment.
Penerapan Random Forest dan Content-Based Filtering pada Alokasi Tenaga Kesehatan Hipertensi Jefry Sunupurwa Asri; Diah Aryani; Puteri Fannya; Ratna Dewi
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9251

Abstract

Hypertension is a major health issue in DKI Jakarta requiring efficient resource distribution to overcome inter-regional access inequalities. This research aims to design and implement a web-based decision support system (DSS) integrating Geographic Information System (GIS) to optimize health worker allocation and determine hypertension priority areas precisely. The novelty lies in integrating a Random Forest machine learning model to predict service coverage until 2030 with Content-Based Filtering (CBF). The CBF method utilizes intrinsic regional features, including service percentages, geographical locations, and prediction trends, to generate objective health worker quota recommendations. The Random Forest model was validated using 5-Fold Cross Validation with excellent performance, showing an average R² value of 0.86 and an accurate Mean Absolute Error (MAE) of 6.7%. The system is implemented using Streamlit and Folium frameworks for geographical visualization. Research results provide contributions through priority area maps, adaptive health worker quota recommendations, and Mobile Health Clinic route simulations supporting data-driven decision-making. Through this system, policymakers can perform strategic planning to improve hypertension intervention effectiveness in Jakarta. With an integrated predictive and recommendation approach, this study is expected to become a reference in the digital transformation of public health resource allocation more equitably and accurately.
Perbandingan Performa Xception dan InceptionV1 untuk Pengenalan Ekspresi Wajah Ferdinand Defin Delio; Diah Aryani; Habibullah Akbar; Mohamad Yusuf; Yaya Sudarya Triana
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.i1.003

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan performa dua arsitektur Convolutional Neural Network (CNN) populer, yaitu Xception dan InceptionV1, dalam tugas pengenalan ekspresi wajah (Facial Expression Recognition/FER). Penelitian ini dilakukan dengan pendekatan transfer learning dan fine-tuning menggunakan dataset FER-2013 yang berisi 35.887 citra wajah grayscale berukuran 48×48 piksel yang diklasifikasikan ke dalam tujuh emosi dasar. Setiap citra diubah ukurannya menjadi 224×224 piksel, dinormalisasi, dan diproses dengan teknik augmentasi untuk meningkatkan generalisasi model terhadap variasi ekspresi wajah, pencahayaan, dan pose. Proses pelatihan dilakukan selama 30 epoch menggunakan optimizer Adam dengan learning rate 0.0001 dan batch size 64. Strategi fine-tuning dilakukan dengan membuka 30% lapisan atas model untuk mengoptimalkan bobot fitur yang telah dipelajari sebelumnya dari dataset ImageNet. Evaluasi kinerja dilakukan berdasarkan metrik akurasi, presisi, recall, F1-score, serta efisiensi komputasi yang diukur dari waktu pelatihan dan inferensi. Hasil eksperimen menunjukkan bahwa Xception mencapai akurasi validasi 70,69% dengan waktu inferensi rata-rata 20–25 ms, sedangkan InceptionV1 mencapai 65,80% dengan waktu inferensi 43–126 ms. Arsitektur Xception terbukti lebih efisien secara komputasi karena memanfaatkan depthwise separable convolution yang mengurangi jumlah parameter tanpa menurunkan akurasi. Temuan ini menunjukkan bahwa Xception lebih sesuai untuk aplikasi FER real-time dan perangkat dengan sumber daya terbatas, serta memberikan dasar yang kuat bagi penelitian lanjutan dalam pengembangan sistem pengenalan ekspresi wajah berbasis video dan lingkungan dunia nyata.
KLASIFIKASI SENTIMEN ULASAN PENGGUNA HALODOC MULTIPLATFORM MENGGUNAKAN LSTM DENGAN PELABELAN INDOBERT Dian Dwi Pramesti; Nizirwan Anwar; Diah Aryani; Jefry Sunupurwa Asri
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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Abstract

Pertumbuhan layanan telemedicine di Indonesia menghasilkan volume ulasan pengguna yang besar pada toko aplikasi dan media sosial.Ulasan tersebut memuat pengalaman. kritik. serta saran yang dapat digunakan untuk mengevaluasi kualitas layanan. tetapi jumlahnyamembuat analisis manual menjadi tidak efisien. Penelitian ini menerapkan Long Short-Term Memory (LSTM) untukmengklasifikasikan sentimen ulasan Halodoc yang dihimpun dari Google Play Store dan platform X. Dataset gabungan berisi 20.535teks. terdiri atas 10.000 ulasan Google Play Store dan 10.535 unggahan X. Tahap pengolahan meliputi case folding, cleansing,normalisasi kata, tokenisasi, dan penghapusan stopword. Label sentimen positif, netral, dan negatif dibentuk secara otomatismenggunakan IndoBERT, kemudian data dibagi menjadi 80% data latih dan 20% data uji untuk pelatihan model LSTM. Hasilpengujian menunjukkan accuracy 86,12%, precision 86,14%, recall 86,12%, dan F1-score 86,12%. Audit terhadap berkas data mentahjuga menunjukkan heterogenitas panjang teks antarsumber dan keberadaan duplikasi persis yang perlu diperhatikan untuk mencegahkebocoran data. Secara umum, LSTM mampu mengklasifikasikan sentimen pada data multi-platform dengan performa yang konsisten,sedangkan validasi manual terhadap label IndoBERT dan pengendalian duplikasi menjadi langkah penting untuk memperkuat validitashasil
TRADE-OFF PERFORMA DAN EFISIENSI KOMPUTASI TIGA ARSITEKTUR DEEP LEARNING UNTUK KLASIFIKASI PENYAKIT DAUN TEBU SECARA OTOMATIS Kevin Januari; Nizirwan Anwar; Diah Aryani; Azifa Habiba
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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Abstract

Tanaman tebu merupakan komoditas perkebunan strategis yang produktivitasnya kerap terancam oleh serangan penyakit daun.Identifikasi penyakit secara visual manual masih banyak digunakan di lapangan, namun pendekatan ini memakan waktu lama,bersifat subjektif, dan rentan terhadap kesalahan. Deep learning menawarkan solusi otomatisasi klasifikasi penyakit tanaman,tetapi studi yang membandingkan secara komprehensif trade-off antara akurasi dan efisiensi komputasi antararsitektur masihterbatas jumlahnya. Penelitian ini bertujuan menganalisis secara komparatif kinerja DenseNet-121, ConvNeXt-Tiny, danYOLOv11s-CLS dalam mengklasifikasikan delapan kelas kondisi daun tanaman tebu. Metode penelitian menggunakan datasetgabungan sebanyak 4.075 citra yang dibagi secara stratified dengan rasio 70:15:15, serta pendekatan transfer learning berbasisImageNet pada ketiga arsitektur. Evaluasi dilakukan pada tiga dimensi, yaitu kinerja klasifikasi, kemampuan generalisasi, danefisiensi komputasi. Hasil pengujian menunjukkan DenseNet-121 memperoleh accuracy tertinggi sebesar 93,14% dan F1-score macro 91,09%, ConvNeXt-Tiny unggul pada precision macro sebesar 92,59%, sedangkan YOLOv11s-CLS mencatatkanaccuracy 92,81% dengan jumlah parameter, ukuran model, FLOPs, dan waktu inferensi terendah yaitu 4,70 milidetik sertageneralization gap terkecil sebesar 0,0027. Penelitian ini menyimpulkan bahwa DenseNet-121 sesuai untuk skenario yangmemprioritaskan akurasi, sedangkan YOLOv11s-CLS direkomendasikan sebagai model dengan keseimbangan performa danefisiensi terbaik untuk implementasi pada perangkat berdaya komputasi terbatas.
IMPLEMENTATION OF DEVOPS APPROACH USING CI/CD WITH A SHARED-PIPELINE TO ACCELERATE WEB SERVICE DEVELOPMENT Ahmad Lukman Hakim; Diah Aryani; Daniati Uki Eka Saputri
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8208

Abstract

The development of microservices-based web service applications requires fast, consistent, and automated integration and deployment processes. However, in practice, many organizations still rely on manual procedures and non-standardized pipeline configurations, which lead to release delays and increase the risk of operational errors. The DevOps approach through Continuous Integration and Continuous Delivery (CI/CD) has emerged as a relevant solution. Nevertheless, its practical implementation still requires clear and reusable guidance. This study aims to implement DevOps approach using CI/CD practices for the backend microservices at JEL Tech Inc by leveraging GitLab and a shared-pipeline scheme. The research adopts an applied research methodology with a case study approach, following the stages of the DevOps lifecycle, including plan, code, build, test, release, deploy, operate, and monitor. Pipeline automation is constructed using modular shell scripts executed by GitLab Runner and integrated with Docker, a Container Registry, and Kubernetes. The results demonstrate that the implementation of a shared-pipeline successfully standardizes build, package, and deployment processes across multiple microservices repositories. The implementation results show measurable improvements, including up to 90% reduction in deployment time, 400% increase in release frequency, and significant reduction in failure rates. This study contributes a reusable shared-pipeline architecture that extends existing DevOps implementation practices by providing a standardized, modular, and testable CI/CD model for microservices environments.
When Blogging Meets News Writing: Indonesian EFL Student Teachers’ Experiences in a Journalism Course Dwi Sloria Suharti; Syaadiah Arifin; Diah Aryani; Hani Dewi Ariessanti
International Journal of Educational Research Vol. 3 No. 2 (2026): June: International Journal of Educational Research
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijer.v3i2.504

Abstract

This study explores Indonesian EFL student teachers’ experiences of learning to write news articles through blogging in a Journalism course. The study was motivated by the need to provide meaningful and genre-based writing practice for EFL student teachers, who often face difficulties in generating ideas, organizing information, meeting genre expectations, and writing for authentic aences. Employing a qualitative case study design, the study involved twenty undergraduate student teachers from a private Islamic university in Tangerang, Indonesia; all participants completed an online quetaire, and five volunteers joined follow-up semi-structured interviews. The questionnaire data were alysed descriptively, while the interview data were examined thematically. The findings indicate that blogging supported regular writing practice, helped students understand news text structure, enouraged clearer organization of headlines, leads, and supporting details, and increased engagement by allowing students to publish and share their work. Blogging also promoted peer interaction and selfevaluation because students could read, compare, and comment on one another’s posts. However, some paticpants eprienced challenges related to technical blogging skills, uncertainty about writing standards, limited peer feedback, and discomfort with public online publication. The study concludes that blogging can serve as a useful platform for EFL news writing when it is supported by explicit istrucion, relevant tasks, clear assessment criteria, structured feedback, and ethical awareness in using digital and AI-driven tools.
Deteksi Banjir Area Perkotaan Berbasis Citra Digital dan Convolutional Neural Network (VGG19) Habibullah Akbar; Diah Aryani; Muhamad Bahrul Ulum
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 4 No. 3 (2024): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v2i3.798

Abstract

Geographically and demographically, Indonesia has natural conditions that have the potential for floods disaster. There are at least 16,771 islands and 65,017 rivers that fill the archipelago. Unfortunately, the ever-increasing urban population accompanied by a lack of awareness and preparation for protecting the environment has resulted in a higher risk of flooding in urban areas. This study utilizes digital imagery to detect flood conditions in urban areas. In terms of access, digital images are available in urban CCTV monitoring systems as well as office areas, housing, and from people who have smartphones. The detection method used in this study is the VGG19 model which consists of 16 convolution layers and 3 standard classification layers. All convolution layers are divided into 5 blocks followed by a MaxPooling layer for each block to reduce the resolution of the input image. In the last layer, SoftMax layer is used to estimate the probability between flood labels and normal conditions. There are 4 parameters that were optimized during the VGG19 model training process, namely Batch Size, Learning Rate, Dropout and Epoch (training repetition). To test the proposed model, public datasets are used, namely the Roadway Flooding Image Dataset and Road Vehicle Images Dataset. The best flood detection results (or normal conditions) achieve the accuracy of 98.78%. As for the other three performance metrics, namely precision, recall and F1-score, they reach 99%. These results are generated by the VGG19 model with a Batch Size parameter of 20, a Learning Rate of 1e-5 (0.00001), 50% Dropout and 100 Epoch. The achievement values of the four metrics can be considered quite good, so that the VGG19 model has the opportunity to be developed for flood detection applications in order to monitor urban flood conditions.
Komparasi Naive Bayes dan Support Vector Machine dalam Mendeteksi Sarkasme Teks Twitter (X) Syifa Azzahra; Bayu Sulistiyanto Ipung Sutejo; Diah Aryani; Jefry Sunupurwa Asri
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i04.2326

Abstract

The use of non-standard language on social media has become one of the challenges in detecting sarcasm because the meaning conveyed is not always recognizable based solely on the words present in the text. This study aims to compare the performance of Naive Bayes and Support Vector Machine (SVM) in detecting sarcasm in non-standard Indonesian Twitter text (X). The Wilcoxon Signed-Rank Test is used to test the significance of the performance difference between the two algorithms. The audit results in 15,072 unique tweets, consisting of 12,057 training data and 3,015 test data. The test results show that Naive Bayes achieved an accuracy of 69.45%, precision of 67.66%, recall of 64.19%, and F1-Score of 65.88%, while SVM achieved an accuracy of 68.39%, precision of 66.51%, recall of 62.82%, and F1-Score of 64.61%. In the 5-fold cross-validation, Naive Bayes produced an average accuracy of 68.74% with a standard deviation of ±0.33%, while SVM achieved 68.17% with a standard deviation of ±1.14%. The Wilcoxon Signed-Rank Test results in a p-value of 0.4375 (>0.05), indicating that the performance difference between the two algorithms is not statistically significant. Thus, Naive Bayes shows slightly higher evaluation results numerically, but it cannot yet be stated that it has significantly superior performance compared to SVM on this research dataset.
Design and Construction of a Swimming Pool Reservation System Based on Next.js and REST API Fatur Rahman; Diah Aryani
ICIT (Innovative Creative and Information Technology) Journal Vol. 12 No. 02 (2026): August 2026
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/icit.v12i02.4008

Abstract

This research project aims to improve the manual reservation method still used at the Raihan Islamic Swimming Pool in Tangerang by creating a web-based pool reservation and monitoring system using Next.js technology and REST API. Issues such as slow service, inaccurate record keeping, and a lack of information transparency often reduce customer satisfaction and operational effectiveness. This research adopted an Extreme Programming approach in the system development process, collecting data through literature reviews, interviews, and observations. The results of the implementation based on black box testing indicate that this system is able to provide direct access to information and automate the booking and payment process through Midtrans, so that the entire reservation process can be carried out more efficiently, quickly, and integrated without significant obstacles. The use of PWA, updateable news components, and integration with Google login also contribute to an improved user experience and convenience. Although it needs development in several areas, such as automatic cancellation features and reporting, this system is believed to be very useful for improving efficiency, control, and service quality.
Co-Authors Adaafi'ah, Yasinta Addaafi’ah, Yasinta Ade Setiadi Adnandi, M Arba Adrian Adrian Ahmad Fathurrozi Ahmad Lukman Hakim Aji Setiawan Akbar, Habibullah Andreansyah, Tedy Andriyanti Asianto Ari Asmawati Ari Pambudi Arnas, Dio Hartawan Ary Budi Warsito, Ary Budi Azifa Habiba Bashir, Pandu Bayu Sulistiyanto Ipung Sutejo Budi Tjahjono Damayanti, Prisila Daniati Uki Eka Saputri Delio, Ferdinand Defin Dian Dwi Pramesti Endi Rekarti, Endi Fannya, Puteri Fatonah, Nenden Siti Fatur Rahman Ferdinand Defin Delio Fifit Alfiah Fredy Susanto Gaol, Lasando Lumban H. Haris H. Haris Hani Dewi Ariessanti Harahap, Eka Purnama Haris Haris ., Haris Harlinda Syofyan Hendrian Hendrian Hendrian, Hendrian Herwanto, Agus Ichwani, Arief Ichyan Holilan Ilamsyah Ilamsyah Indri Handayani, Indri Ira Tyas Ningrum Izza Ilma Insyifani Jefry Sunupurwa Asri Kevin Januari Kusmawan, Dicky Luri Sesy Apriyani Malabay Malabay Manurung, Jhordy Noverdian Mariyana Widiastuti Martino Wibowo Martono, Aris Mohamad Yusuf Mohamad Yusuf Mohammed Al-shammari, Marwan Kadhim Muhamad Yusup Muhammad Hadi Arfian Nizirwan Anwar Noviandi Noviandi Po Abas Sunarya Purnomo, Yunianto Putra, Syahrizal Dwi Putri, Dian Mustika Qurotul Aini Rafael Praseli Rafi Zhafar Kurnia Ramadhan, Kresna Ratna Dewi Ronald Nur Sunarto RR. Ella Evrita Hestiandari Ruli Supriati, Ruli Sabar Sukarno Sakina Nusarifa Tantri Saputra, Mochamad Adi Selvana Arma Putra Setiadi, Ade Shine Pintor Siolemba Patiro Sigit Santoso Siti Maesaroh STMIK, Author Super Sudaryono Sudaryono Sugeng Santoso Suhandi Junaedi Suharti, Dwi Sloria Supriyono, Ignatius Agus Suryana, Endang Susanto, Ratnawati Sutanto, Imam Syaadiah Arifin Syafnidawati, Syafnidawati Syifa Azzahra Tiara Fanny Eldiana Ulum, M. Bahrul Ulum, Muhamad Bahrul Untung Rahardja Verdi Yasin Wahyu, Sawali Widiarso, Rafif Tri Widyawan, Tri Ismardiko Willi Kornellius Yaya Sudarya Triana Yose Amelia, Sabrina Yulhendri Yulhendri