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All Journal JUITA : Jurnal Informatika JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi Briliant: Jurnal Riset dan Konseptual Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science CONSEN: Indonesian Journal of Community Services and Engagement METRIK SERIAL TEKNOLOGI DAN SAINS
Zarnelly Zarnelly
Jurusan Sistem Informasi Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Physics Social Sciences Other

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Influence of User Satisfaction of the Halodoc Mobile Application using the End User Computing Satisfaction (EUCS) and DeLone and McLean methods Wulandari, Suri; Munzir, Medyantiwi Rahmawita; Rozanda, Nesdi Evrilyan; Zarnelly, Zarnelly
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.4035

Abstract

PT. Media Dokter Investama is one of several technology companies that provides health consultation services in Indonesia through Mobile Health Technology (MHealth Tech) products. Mobile application. It was recorded that in October 2023 the Halodoc application on Playstore received a rating of 4.8 on a scale of 5 with more than 426 thousand reviews from users. The level of use of the Halodoc application is very high, giving rise to several negative ratings. As in the reviews from rating 2 or not liking the application as much as 658 or 5.76%, and rating 1 or really not liking the application as many as 1,577 reviews or 13.78%. From these results it can be seen that there are still some users who are dissatisfied with the Halodoc application. This research was conducted to measure the influence of user satisfaction of the Halodoc application using the End User Computing Satisfaction and DeLone and McLean methods and to provide research recommendations which can be an input for Halodoc application managers to manage and improve the Halodoc application in the future.
Analisis Loyalitas Pelanggan Business To Business Berdasarkan Model RFM Menggunakan Algoritma Fuzzy C-Means: Business to Business Customer Loyalty Analysis Based on RFM Model Using Fuzzy C-Means Algorithm Al-Yasir, Al-Yasir; Afdal, M.; Zarnelly, Zarnelly; Marsal, Arif
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.1163

Abstract

PT. XYZ merupakan perusahaan yang bergerak di bidang distributor atap plastik dan Aluminium Composit Panel (ACP) yang mengadopsi model usaha B2B. Saat ini strategi yang digunakan oleh PT. XYZ masih belum berfokus pada segmentasi pelanggan dan masih memperlakukan setiap pelanggan dengan sama. Selain itu data penjualan yang terdapat ribuan lebih riwayat transaksi hanya digunakan sebagai arsip yang seharusnya dapat digunakan untuk pengembangan strategi perusahaan. Berdasarkan hal tersebut, penelitian ini melakukan segmentasi pelanggan pada PT. XYZ menggunakan model RFM dan algoritma FCM untuk menganalisis pelanggan bersasarkan karakteristik dan perilakunya. Data yang digunakan terdiri dari 9163 transaksi yang memuat 494 pelanggan. Untuk mendapatkan jumlah cluster yang optimal maka dilakukan pengujian pada jumlah cluster yaitu 2-10. Hasilnya menunjukkan 2 cluster sebagai jumlah yang terbaik dengan nilai DBI 0,4908. Cluster 1 yang terdiri dari 387 pelanggan dikategorikan sebagai loyal customer sedangkan cluster 2 yang terdiri dari 107 pelanggan dikategorikan sebagai lost customer. Sebagai pelanggan yang loyal, perusahaan perlu memberikan apresiasi untuk mempertahankan hubungan baik dengan pelanggan seperti memberikan diskon, ataupun penawaran khusus. Kemudian untuk segmen lost customer, perusahaan perlu mengambil langkah yang tepat untuk mencoba memulihkan hubungan dengan pelanggan dan menganalisis faktor dan penyebab pelanggan pada segmen ini beralih ke perusahaan lain.
Implementasi Metode WASPAS Pada Sistem Pendukung Keputusan Penilaian Kinerja Perawat Terbaik Pratama, Arya Yendri; Muttakin, Fitriani; Permana, Inggih; Zarnelly, Zarnelly; Marsal, Arif
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5068

Abstract

Hospitals are health service institutions that provide various services to the community, including inpatient, outpatient and emergency care. Hospitals as health service institutions require optimal nurse performance in providing quality services to patients. At XYZ Hospital, assessments and calculations are still carried out manually, often experiencing difficulties because in carrying out the assessment all the calculation data is carried out one by one, resulting in large errors and taking quite a long time to obtain the decision results and it is difficult to carry out rankings due to frequent assessment forms. scattered. There are 14 criteria for assessing nurse performance, namely loyalty/loyalty, work performance, responsibility, obedience/discipline, honesty, cooperation, communication, knowledge, competency I nurse (PK I), competency II nurse (PK II), competency III nurse (PK III), the presence of hand washing in the room, the quality of the work carried out by the person concerned, and the availability of ready-to-use facilities & infrastructure for the next shift. To obtain accurate performance assessment results, a decision support system was created using the WASPAS method. The WASPAS method is said to be appropriate for selecting the best nurses because it is ranked based on specified criteria values. It is hoped that the research carried out will help obtain effective results. In this research, the results obtained were that the best nurse at XYZ Hospital was the alternative with a score of 50,038 in the name of EET.
Analisis Sentimen Masyarakat Terhadap Liga Indonesia Menggunakan Algoritma Naïve Bayes Classifier dan Support Vertor Machine Pada Platform X dan YouTube Irwanda, Mahyuda; Afdal, M; Novita, Rice; Zarnelly, Zarnelly
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7294

Abstract

The Indonesian League is a national football competition that attracts a lot of public attention. However, various problems such as controversial referee decisions, fan riots, and match-fixing issues are often in the spotlight. This study aims to analyze public sentiment towards the Indonesian League using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. Data were collected from social media platform X (Twitter) as many as 2000 tweets and YouTube as many as 2000 comments in the period from January 2023 to December 2024. After going through preprocessing stages such as cleaning, case folding, tokenizing, stopword removal, and stemming, the data was classified into positive, negative, and neutral sentiments. The results showed that SVM had a higher accuracy (99%) than NBC (85%) in sentiment analysis.
PREDICTION OF ANEMIA USING THE PARTICLE SWARM OPTIMIZATION (PSO) AND NAÏVE BAYES ALGORITHM tri utami, septiana; Sriyanto, Sriyanto; Lestari, sri; Widi Nugroho, Handoyo; zarnelly, zarnelly
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.28428

Abstract

Purpose: Anemia is a nutritional disorder that is still often found in Indonesia. The main risk factors for iron deficiency anemia are low iron intake, poor iron absorption, and periods of life when the need for iron is high such as during growth, pregnancy, and breastfeeding. Anemia can generally occur in pregnant women, teenagers, the elderly and even babies who have anemia.Methods/Study design/approach: This research uses the Naive Bayes and PSO algorithms, and the dataset used comes from the kaggel.com Anemia dataset. The number of data records is 1421 data consisting of 5 attributes and 1 label. This data set is used to predict whether a patient is likely to suffer from anemia.Result/Findings: Based on the results of testing the Naïve Bayes and PSO algorithm models which were carried out through confusion matrix evaluation, it was proven that the tests carried out by the Naïve Bayes algorithm were 93.88% and the tests carried out with Naïve Bayes and PSO had a high accuracy value, namely 94.02%.Novelty/Originality/Value: The purpose of selecting information acquisition features is to select features or attributes that significantly influence anemia. Keywords: Prediction, Anemia, Naive Bayes, Particle Swarm Optimization (PSO)
ANALISIS PENERAPAN ALGORITMA DECISION TREE DALAM KEAMANAN SIBER UNTUK KELASIFIKASI SITUS WEBSITE PHISHING S. Nagalay, Fitra Salam; Sriyanto, Sriyanto; Zarnelly, Zarnelly
Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Vol 10, No 1 (2024): Februari
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/rmsi.v10i1.28401

Abstract

Dalam era teknologi yang semakin berkembang cepat dan ketergantungan masyrarakat pada internet, ancaman terhadap keamanan siber semakin beragam. Salah satu ancaman yang menonjol adalah kegiatan phising, dimana pihak tidak bertanggungjawab menggunakan alamat elektronik atau situs palsu untuk mendapatkan informasi peribadi pengguna. Ancaman phising tersebut tidak hanya mengancam keuangan, tetapi juga privasi pengguna. Penelitian ini menerapakan algoritma Decision Tree dalam klasifikasi. Algoritma Decision Tree terpilih karena kemudahan pemahaman dan interpretasinya serta kemampuannya dalam melakukan klasifikasi dengan baik. Fokus penelitian ini adalah mendalami potensi algoritma Decision Tree dalam mendeteksi situs website phising dengan menerapkan fitur-fitur spesifik seperti panjang URL, rangking, durasi aktif, dan karakteristik lainnya. Eksperimen dilakukan dengan menggunakan dataset yang mencakup berbagai fitur terkait URL dan dilakukan evaluasi menggunakan metrik seperti akurasi, presisi, dan recall. Hasil penelitian menunjukan bahwa model Decision Tree yang dikembangkan berhasil mencapai tingkat akuraasi sebesar 87.04%, memberikan kontribusi positif terhadap upaya mengamankan pengguna dari situs website phising.Kata kunci: Decison Tree, Klasifikasi, Keamanan Siber, Website Phishing
Evaluasi Pengalaman Pengguna Website Pemerintah Menggunakan Model User Experience Questionniare (UEQ): Evaluation of Government Website User Experience Using the User Experience Questionnaire (UEQ) Model Megawati, Megawati; Zarnelly, Zarnelly; Aulia Rahman, Hafiz
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 6 No. 1 (2026): Indonesian Journal of Informatic Research and Software Engineering (IJIRSE)
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v6i1.2558

Abstract

Website pemerintah berperan sebagai media utama penyedia informasi publik dan sarana komunikasi antara pemerintah dan masyarakat. Kualitas pengalaman pengguna (User Experience/UX) menjadi faktor penting dalam menentukan efektivitas dan penerimaan layanan digital pemerintah. Penelitian ini bertujuan mengevaluasi pengalaman pengguna pada Website Kementerian Agama Provinsi Riau menggunakan metode User Experience Questionnaire (UEQ). Penelitian melibatkan 68 responden yang pernah mengakses website tersebut. Hasil analisis menunjukkan nilai mean untuk masing-masing skala sebagai berikut: Attractiveness (0,686), Perspicuity (0,688), Efficiency (0,665), Dependability (0,673), Stimulation (0,647), dan Novelty (0,346). Secara absolut, seluruh skala berada dalam kategori netral. Namun, berdasarkan analisis benchmark UEQ, sebagian besar skala termasuk dalam kategori Bad dan Below Average. Temuan ini menunjukkan bahwa meskipun website telah memenuhi fungsi dasar penyampaian informasi, kualitas pengalaman pengguna masih berada di bawah rata-rata produk digital dalam database UEQ. Oleh karena itu, diperlukan perbaikan pada aspek efisiensi, konsistensi desain, pembaruan konten, dan inovasi tampilan untuk meningkatkan kualitas layanan digital pemerintah.  
Evaluation of CNN Architectures for Kidney Stone Classification in Ultrasound Image Zuriati, Zuriati; Sriyanto, Sriyanto; Supriyatna, Agiska Ria; Qomariyah, Nurul; Afifah, Dian Ayu; Zarnelly, Zarnelly
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kidney stone diagnosis requires fast and reliable evaluation, yet ultrasound interpretation still largely depends on clinical expertise. This study evaluates four Convolutional Neural Network (CNN) architectures, VGG16, ResNet50, MobileNetV2, and EfficientNetB0 for classifying kidney ultrasound images into Normal and Stone categories. Using a public dataset of 9,416 images, the models were assessed in terms of predictive performance and computational efficiency. MobileNetV2 achieved perfect classification performance, recording 100% accuracy, precision, recall, and F1-score, while maintaining the smallest parameter size (≈3.6M) and fastest training time (~44 s/epoch). VGG16 and ResNet50 also delivered near perfect accuracy (99.79% and 99.89%) with full recall for Stone cases. In contrast, EfficientNetB0 failed to generalize, yielding only 51.62% accuracy due to severe misclassification of Normal images. These results demonstrate that MobileNetV2 provides the most reliable and efficient solution for ultrasound based kidney stone classification, highlighting its strong potential for practical clinical deployment.
Co-Authors 'alimah, Muta Afifah, Afifah Pendri Afifah, Dian Ayu Afifah, Tiara Afrah Ahsyar, Tengku Khairil Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Al-Yasir, Al-Yasir Alfian, Zhevin Anahyu, Yelfi Dwi Angraini Angraini Anjani, Yulia Merry Anofrizen Anofrizen Ariansah, Rizky Arif Marsal Aulia Rahman, Hafiz Butar Butar, Febiola Siska Dulbari, Dulbari Eki Saputra fitriano, ernesto Hamzah, Muhammad Luthfi Harmutika, Della Inggih Permana Irwanda, Mahyuda Jazman, Muhammad M. Afdal Megawati , Megawati Megawati - Mona Fronita, Mona Munandar, Darwin Munzir, Medyantiwi Rahmawita Mustakim Mustakim Muttakin, Fitriani Nazaf, Latiful Nurfadilla, Nadia Nurul Qomariyah Permana, Jeki Harya Pratama, Arya Yendri Priady, Muhamad Ilham Pristiawati, Andani Putri Putra, Ahdika Sawiyya Putra, Tandra Adiyatma Rahman, Eman Rahmawita, Medyantiwi Ramadhani, Indah Razmi, Fikri Rice Novita Rozanda, Nesdi Evrilyan S. Nagalay, Fitra Salam Siti Monalisa Sri Lestari Sriyanto Sriyanto Sriyanto, Sriyanto Supriyatna, Agiska Ria Suwito, Bagus Anjari Syaifuddin, Aditya tri utami, septiana Ula, Walid Alma Widi Nugroho, Handoyo Widyawati, Dewi Kania Wulandari, Suri Yanti, Rahma Zulkarnain Zulkarnain Zuriati, Zuriati