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All Journal TEKNIK INFORMATIKA Techno.Com: Jurnal Teknologi Informasi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal sistem informasi, Teknologi informasi dan komputer Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi Jurnal Ilmiah FIFO JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) International Journal of Community Service Learning IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Ensiklopedia of Journal IJISCS (International Journal Of Information System and Computer Science) Aptisi Transactions on Technopreneurship (ATT) JSAI (Journal Scientific and Applied Informatics) Journal of Information Systems and Informatics Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pasopati : Pengabdian Masyarakat dan Inovasi Pengembangan Teknologi Jurnal Teknik Informatika (JUTIF) IJISTECH RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Bulletin of Computer Science Research Journal of Informatics Management and Information Technology Journal of Social Responsibility Projects by Higher Education Forum KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal ABDIMAS Budi Darma Jurnal Ilmu Komputer Dan Informatika Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Dedikasi Nusantara: Jurnal Pengabdian Masyarakat Pendidikan Dasar Sinergi: Jurnal Pengabdian Kepada Masyarakat Jurnal Rekayasa Sistem Informasi dan Teknologi Journal of Information Systems Management and Digital Business CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science Jurnal Pengabdian Masyarakat Nasional JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) ORAHUA : Jurnal Pengabdian Kepada Masyarakat IJISCS (International Journal of Information System and Computer Science) INTECH - Informatika Dan Teknologi
Jumaryadi, Yuwan
Universitas Mercu Buana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Analisis Sentimen Ulasan Pengguna QRIS pada Aplikasi GoPay: Studi Komparatif Algoritma Support Vector Machine dan Decision Tree Berbasis TF–IDF Erlinda Sistia Aritonang; Yuwan Jumaryadi
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9582

Abstract

This study aims to analyze user sentiment towards the QRIS feature in the GoPay application based on reviews on the Google Play Store and to build a sentiment classification model using a machine learning approach. A total of 20,746 reviews were collected and filtered using QRIS-related keywords, resulting in 4,347 relevant reviews. The data were manually labeled and preprocessed, then extracted using the TF–IDF method. The analysis results show a sentiment distribution consisting of 49.49% positive, 35.34% negative, and 15.18% neutral. The classification process was carried out using the Support Vector Machine (SVM) and Decision Tree algorithms. The evaluation results showed that Decision Tree achieved 79% accuracy with precision, recall, and F1-score values ​​of 79% each, while SVM produced 78% accuracy with precision of 79%, recall of 78%, and F1-score of 78%. The difference in performance between the two models was relatively small, so both had equal capabilities in sentiment classification, although Decision Tree showed slightly better metric consistency.
Machine Learning Approaches to Sentiment Analysis of Mental Health Discussions on Platform X Yuwan Jumaryadi; Riri Fajriah; Umniy Salamah; Bagus Priambodo; Arie Lystha
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11350

Abstract

Sentiment analysis on mental health issues is crucial for understanding public perceptions of healthcare services. This study analyzed tweets related to mental health on platform X in 2025 using SVM, Random Forest, and Naive Bayes algorithms. Data was collected through web scraping with Python, then evaluated using a confusion matrix with accuracy, precision, f1-score, and recall metrics. The classification results showed a distribution of sentiment: positive (3,667 tweets), neutral (838 tweets), and negative (704 tweets). A comparative analysis of the three algorithms revealed that SVM achieved the highest accuracy (78.69%), followed by Random Forest (75.04%) and Naive Bayes (70.44%), proving the superiority of SVM in classifying mental health sentiment. These findings provide valuable insights for stakeholders in improving mental healthcare services based on public feedback, while also offering a reference for effective sentiment analysis methods for social media data.
Implementation of Proxy at XYZ Inc. Study: Experiment on Network Performance Optimization Fandi Ali Mustika; Muhammad Rifqi; Yuwan Jumaryadi; Febryo Ponco Sulistyo; Indah Ramadhani; Eko Prasetyo Pratomo
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11535

Abstract

The rapid development of information technology demands companies to have a network infrastructure that is not only fast but also secure and efficient. PT. XYZ, as a company engaged in distribution and customer service, faces challenges in managing increasing data traffic while maintaining the stability and security of its internal network. One strategic solution to address this issue is the implementation of a proxy server. A proxy server functions as an intermediary between users and the internet, enabling it to filter data requests, store cache, and restrict access to unwanted content. Thus, the implementation of a proxy server can improve bandwidth efficiency, accelerate access to frequently used websites, and strengthen network security systems through traffic monitoring and restriction. This research aims to implement and evaluate the effectiveness of using a proxy server within PT. XYZ’s environment. The evaluation results show that the use of a proxy server can enhance network efficiency and provide better protection against cyber threats. With a more controlled and secure system, the company can run its operations more optimally and sustainably.
Water Quality Measurement based on Internet of Thing Misbahul Fajri; Yuwan Jumaryadi; Anne Parlina
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11642

Abstract

Good water quality is crucial for living things, including temperature, pH, and TDS, which are constantly changing due to various factors. These three water parameters are crucial for maintaining water quality within a certain threshold to ensure that an ecosystem meets specified standards. Measuring water quality is essential to anticipate these changes as desired. Internet of Things (IoT) technology allows continuous monitoring of water parameters at any time and can be accessed anywhere with a network connection via computer or smartphone. In this proposed research, an IoT-based system based on ESPHome will be developed for water quality measurement in aquarium water and its ecosystem. The proposed research detects, records, and displays water pH and TDS parameters, including temperature, using an ESP8266 microcontroller. The system utilizes sensors to detect water parameters; the system utilizes an ESP8266 microcontroller and a WiFi connection that sends data to a cloud-based server with a Homeassistant dashboard. The research results are well-functioning in both hardware and software and are easily accessible.
Multimodal Transfer Learning for Anti-Inflammatory Medicinal Plant Leaf Classification using ResNet50 Umniy Salamah; Nur Ani; Yuwan Jumaryadi; Agustiawan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12279

Abstract

This study aimed to develop an AI-based image classification model using transfer learning methods to identify seven types of anti-inflammatory plant leaves commonly used in traditional medicine. The novelty of this research lies in approach to integrating Canny Edge detection and Gamma Correction with the ResNet50 architecture for multimodal fusion. The class plants, including Aloe Vera, Annona Muricata, Centella Asiatica, Muntingia Calabura, and Ocimum Basilicum, are known for their therapeutic properties and bioactive compounds. A dataset consisting of 350 images per species was collected, with images divided into training (70%), validation (20%), and testing (10%) sets. Data augmentation techniques such as rotation, flipping, and zooming were applied to improve model generalization. To enhance classification performance, pre-trained convolutional neural network (CNN) models, including ResNet50 and VGG16, were employed for transfer learning. The study also integrated image processing techniques, such as the Laplacian Filter, Canny Edge, and Gamma Correction, to extract additional features and improve the model’s accuracy. Among the different configurations tested, the combination of Canny Edge and Gamma Correction with ResNet50 yielded the best results, achieving a training accuracy of 89.3%, validation accuracy of 88.1%, and test accuracy of 87.0%. In contrast, the use of Laplacian Filter and Canny Edge with ResNet50 led to lower performance, suggesting that multimodal fusion of certain feature extraction methods could enhance classification accuracy. This research highlighted the potential of AI-driven approaches in the classification of medicinal plant leaves and offered a more efficient, accurate method for identifying anti-inflammatory plants used in traditional medicine.
SOSIALISASI APLIKASI PENDETEKSI DIABETES DILENGKAPI DENGAN LIE DETECTION UNTUK MASYARAKAT KELURAHAN DURI KEPA Umniy Salamah; Yuwan Jumaryadi; Diky Firdaus; Bagus Priambodo; Afifah Fitri Anggraini; Vivie Herlina; Ayaitulla Salsabilla Achmad; Putra Ardiansyah; Romeo Mulia Pratama; Zehandra Gibran Nugroho
Jurnal Pengabdian Masyarakat Nasional Vol. 6 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v6i1.36927

Abstract

Diabetes Melitus Tipe 2 (DMT2) merupakan salah satu permasalahan kesehatan utama di wilayah perkotaan dan banyak dialami oleh kelompok usia produktif, termasuk masyarakat di Kelurahan Duri Kepa. Rendahnya pemahaman masyarakat, khususnya ibu-ibu PKK, mengenai deteksi dini diabetes menjadi latar belakang pelaksanaan kegiatan Pengabdian kepada Masyarakat (PkM) yang berfokus pada sosialisasi aplikasi pendeteksi risiko diabetes berbasis teknologi lie detection. Kegiatan yang dilaksanakan pada 10–31 Oktober 2025 ini meliputi tahap persiapan, pelaksanaan, evaluasi, dan tindak lanjut, dengan melibatkan partisipasi aktif masyarakat. Aplikasi yang diperkenalkan memanfaatkan analisis respons pengguna untuk mengidentifikasi potensi risiko diabetes dan memberikan kategori hasil berupa Tipe 1, Tipe 2, atau Tidak Terdeteksi. Hasil yang diberikan bukan merupakan diagnosis medis, tetapi indikator awal yang berfungsi sebagai pengingat agar masyarakat lebih memperhatikan pola makan, aktivitas fisik, dan gaya hidup sehat. Evaluasi kegiatan menunjukkan adanya peningkatan pengetahuan dan kesadaran masyarakat terhadap deteksi dini diabetes serta pemanfaatan teknologi informasi dalam bidang kesehatan. Program ini memberikan dampak positif dalam meningkatkan literasi kesehatan digital dan menunjukkan penerapan inovasi teknologi informasi untuk mendukung upaya pencegahan penyakit tidak menular di lingkungan masyarakat.
Evaluasi Kualitas Sistem Informasi Akademik Dengan Webqual dan IPA Yuwan Jumaryadi; Misbahul Fajri; Bagus Priambodo
JOINS (Journal of Information System) Vol 7 No 2 (2022): Edisi November 2022
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v7i2.6187

Abstract

Peran dari teknologi informasi di suatu institusi sangat dibutuhkan untuk menunjang kinerja karyawan di institusi tersebut. Penggunaan teknologi informasi akan meningkatkan tingkat efisiensi dan eketivitas dari suatu proses. Evaluasi terhadap usabilitas merupakan hal yang penting dalam memberikan rekomendasi terhadap sistem informasi untuk meningkatkan kepuasan pengguna. Penelitian ini melakukan evaluasi terhadap kualitas sistem informasi akademik di salah satu perguruan tinggi di Jakarta menggunakan menggunakan metode Website Quality (WebQual) dan Importance Performance Analysis (IPA). Kuadran I merupakan kuadran dengan prioritas penting, dimana indikator pada kuadran I adalah Usability_2. Pada Kuadran II sistem informasi akademik harus mempertahankan indikator yang ada seperti Inf_Qual_1, Inf_Qual_2, Inf_Qual_3, Inf_Qual_4, Usability_1, Usability_3, dan Usability_4. Pada kuadran III merupakan indikator yang dapat diabaikan kepentingannya, dimana indikator yang terdapat didalamnya adalah Serv_Int_4, Serv_Int_3, Inf_Qual_5, Usability_5, dan Usability_6, dan kuadran IV dinilai memiliki tingkat kepentingan yang rendah namun memiliki tingkat kinerja yang sangat baik dimana indikatornya adalah Serv_Int_1, Serv_Int_2, Usability_7. Berdasarkan penelitian yang dilakukan, maka indikator Usability_2 merupakan hail prioritas agar dapat ditingkatkan untuk meningkatkan kepuasan pengguna terhadap penggunaan sistem informasi akademik.
Implementasi Algoritma Random Forest untuk Analisis Sentimen Ulasan Pengguna Aplikasi Merdeka Mengajar Yuwan Jumaryadi; Ruci Meiyanti; Riri Fajriah; Athiyyah Nisrina Mahsyar; Puspita Sari Anggraeni
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.530

Abstract

Education plays a major role in determining the quality of human resources. The role of teachers is very important as educators who provide guidance and learning. As an effort to facilitate teachers to carry out their duties and responsibilities, especially in the Merdeka Mengajar curriculum, the Ministry of Education and Culture has developed an application called Merdeka Mengajar. However, there is no method to classify sentiment or opinions from comment data on the Merdeka Mengajar application user satisfaction survey on the Google Playstore, in order to determine the extent of user satisfaction with the Merdeka Mengajar application. This study aims to observe sentiment analysis regarding user opinions on the Merdeka Mengajar application on the Google Playstore using the Random Forest, SVM and Naïve Bayes algorithms using TF-IDF weighting for the classification process. This study uses secondary data derived from user reviews of the Merdeka Mengajar application and is classified using the Random Forest, SVM, and Naïve Bayes methods. The results of the classification show that the Random Forest algorithm is the best algorithm in predicting Merdeka Mengajar application user reviews compared to Naive Bayes and SVM.
Sistem Informasi Penilaian Kinerja Karyawan Metode Umpan Balik 360 Derajat Dandi Agih Kusdinar; Ruci Meiyanti; Yuwan Jumaryadi
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9232

Abstract

ITS is a company engaged in the construction sector and routinely conducts employee performance assessments. However, the current assessment process is still subjective because it is only carried out by superiors without the involvement of other parties and without clear assessment criteria to support decision-making. This study aims to develop a web-based employee performance assessment information system by implementing the 360 ​​Degree Feedback method, which allows for comprehensive evaluations from various perspectives, including superiors, coworkers, and subordinates. The system development process uses the Waterfall method, which includes the stages of needs analysis, design, implementation, and testing. Problem analysis is carried out using the Fishbone method to identify the main causes of the ineffectiveness of the previous system. The system design is depicted through UML diagrams, while application development uses the Yii Framework and MySQL database. Functional testing of the system is carried out using the Black Box Testing method to ensure each feature runs as expected. The results of this study are a web-based employee performance assessment information system that assesses based on three main criteria: discipline, technical skills, and personality, resulting in a more objective, accurate, and comprehensive assessment.
Analisis Perbandingan Model Machine Learning untuk Klasifikasi Sentimen Isu Kesehatan Mental Yuwan Jumaryadi
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 4 (2026): Volume 4 Number 4 October 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i4.331

Abstract

Media sosial, khususnya platform X, menjadi salah satu sarana masyarakat dalam menyampaikan opini mengenai berbagai isu, termasuk kesehatan mental. Informasi tersebut dapat dimanfaatkan untuk mengetahui persepsi masyarakat melalui analisis sentimen. Penelitian ini bertujuan membandingkan kinerja beberapa algoritma pembelajaran mesin dalam mengklasifikasikan sentimen terhadap isu kesehatan mental pada platform X. Data dikumpulkan menggunakan teknik web crawling berdasarkan kata kunci "kesehatan mental" selama periode 4 April hingga 28 Mei 2025. Pelabelan sentimen dilakukan secara otomatis menggunakan metode VADER (Valence Aware Dictionary and sEntiment Reasoner) sehingga diperoleh 4.906 data yang terdiri atas 3.415 sentimen positif dan 1.491 sentimen negatif. Tahap preprocessing meliputi cleaning, case folding, normalisasi, tokenizing, stopword removal, dan stemming, kemudian dilakukan ekstraksi fitur menggunakan TF-IDF. Proses klasifikasi dilakukan menggunakan algoritma Support Vector Machine (SVM), Random Forest, Naïve Bayes, dan Logistic Regression dengan pembagian data latih dan data uji sebesar 80:20. Hasil pengujian menunjukkan bahwa algoritma Support Vector Machine (SVM) memberikan kinerja terbaik dibandingkan algoritma lainnya dengan nilai akurasi sebesar 81,57%, presisi 83,29%, recall 81,57%, dan F1-score 79,22%. Hasil penelitian menunjukkan bahwa SVM merupakan algoritma yang paling efektif dalam mengklasifikasikan sentimen terkait isu kesehatan mental pada platform X.
Co-Authors - Arientawati Abdi Wahab Abdul Kholiq Achmad Fathoni Ady Gilang Firmasyah Afifah Fitri Anggraini Afrinaldi Afrinaldi Agung Priambodo Agustiawan Ahmad Wijaya Kusuma Akbar Ramadhan Aldo Prabu Wisnu Wardhana Ali Herdian Alif Muhammad Ihsan Amirullah, Irfan Muflih Anandha Fitriani Andi Nugroho, Andi Andrew Fiade Anita Ratnasari Anne Parlina Aqbar, Harry ARDIANSYAH ARDIANSYAH Arey Nur Warisman Ari Purnadi, Mochamad Ari Sulistiyawati Ari Wibowo Arie Lystha Arief Wibowo Aris Sunandar Athiyyah Nisrina Mahsyar Ayaitulla Salsabilla Achmad Ayumi, Vina Bagus Priambodo Bagus Priambodo Bagus Priambodo Bagus Priambodo Bayu Setiawan Bella Astuti Bella Putri Cahyani Bulan Kirana Subrata Canro Sigalingging Capah, Dwi Ade Handayani Daffa Agrifianto Daim Muhammad Gufron Dandi Agih Kusdinar Danny Yudin Djahidin Dedi Darwis Defriansyah Deni Mahdiana Desi Ramayanti Desmon, Johanes Desnita Nur Fazli Devi Witasari Devy Fatmawati Dian Wirawan Diky Firdaus Dio Dava Ramadha Dio Leonardo Alexandes Dodi Siregar Eko Prasetyo Pratomo Erlin Windia Ambarsari Erlinda Sistia Aritonang Faharuddin, Fadhli Fahmy Umarsyah Faizal Zuli, Faizal Fajriah, Riri Fandi Ali Mustika Farhan Arsyad Firmasyah, Ady Gilang Grace Gata Grace Gata, Grace Harry Agustian Harwikarya Harwikarya Hetty Rohayani Hidayatulloh, Syarief Ida Farida Indah Permata Wulandari Indah Ramadhani Inge Handriani Irfan Muflih Amirullah Ivan Sebastian Jamal Jipesya Jasmir Jeperson Hutahaean Kaburuan, Emil Robert Kolidi Kolidi Linda Miftahul Jannah Lukman Hakim Misbahul Fajri Mohammad Taufan Asri Zaen Muhamad Aldi Rifai Muhamad Fikri Zulfikar Muhammad Fichri Muhammad Rifqi Naufal Bagaskara Naviza Qois Nazrul Azizi Nia Rahma Kurnianda Nia Rahma Kurnianda, Nia Rahma Nirwana Hendrastuty Noprisson, Handrie Nur Ani Nur Ani Nur Ani Nur Iman, Fauzi Nur Ismawati, Nur Nurfadhiilah, Annisa Nurhalizah, Siti Panjaitan, Bosar Ponco Sulistyo, Febryo Ponco Sulistyo Puspita Sari Anggraeni Putra Ardiansyah Radityo Muhamad Rahmad, Khozaeni Bin Randika Aditio Ratna Kusumawardani, Ratna Rendy Akbar Rianto Rheno Budi Setiawan Richard Rinto Priambodo Riri Fajriah Riri Fajriah Riri Fajriah Rizka Ristiana Romeo Mulia Pratama Ruci Meiyanti Salamah , Umniy Salamah, Umniy Samidi Samidi Santoso, Teguh Budi Sari, Yunita Sartika Saruni Dwiasnati Sarwati Rahayu Setiawansyah Setiawansyah Shandy Yosua Sianturi, Heriston Siti Maesaroh Siti Nurhalizah Sitorus, Berlin P Sri Agustiani Br Siburian Sukarno Bahat Nauli Sumanto Sumanto Suryani Dewi Syahdan Rinaldi Kurniawan Syamsir Alam Tanzil Jiro Ahyana Tazkiyah Herdi Tedjo Nugroho Triyono, Gandung Turkhamun Adi Kurniawan Vina Ayumi Vincentius Krisna Aditya Vivie Herlina Wachyu Hari Haji Wahyu Lestari Setyaningsih Wang, Junhai Warisman, Arey Nur Wulan Trisnawati Yaya Sudarya Triana Yustika Erliani Zafina Aisyah Zehandra Gibran Nugroho