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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Informatika KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Angkasa: Jurnal Ilmiah Bidang Teknologi Pelita : Jurnal Penelitian dan Karya Ilmiah Jurnal Informasi dan Komputer Indonesian Journal of Applied Informatics Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Accounting Information System (AIMS) JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) ICIT (Innovative Creative and Information Technology) Journal E-Link: Jurnal Teknik Elektro dan Informatika Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) MEANS (Media Informasi Analisa dan Sistem) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Mahasiswa Sistem Informasi (JMSI) Instal : Jurnal Komputer Jurnal Pengabdian kepada Masyarakat Wahana Usada Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) JURSIMA BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Manajemen Jurnal Accounting Information System (AIMS) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Inovasi dan Teknologi Pendidikan SISFOTENIKA Informasi interaktif : jurnal informatika dan teknologi informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Informatika
Tati Suprapti
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Social Sciences Other

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Peningkatan Kompetensi Guru melalui Pelatihan Google Workspace dalam Pembelajaran Digital Tati Suprapti; Umi Hayati; Abdul Hakim; Abdul Mukhyidin
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

The development of information and communication technology (ICT) requires the world of education to adapt, especially in the learning process. Teachers as the frontline in education must have competence in utilizing ICT, one of which is through the use of Google Workspace. This study aims to improve teachers' competence in utilizing Google Workspace through training activities. The method used is training with a participatory approach and hands-on practice. This activity was carried out in the form of workshops attended by teachers from various levels of education, focusing on the utilization of Google applications such as Google Classroom, Google Drive, Google Docs, and Google Meet. The results of the activity show an increase in teachers' understanding and skills in operating Google Workspace, which has an impact on increasing the effectiveness of online and offline learning. This training also encourages teachers to be more creative in preparing teaching materials, managing digital classes, and building better interactions with students. The conclusion of this activity is that training on the use of Google Workspace is effective in improving teacher competence in the use of learning technology. It is expected that similar activities can be carried out in a sustainable manner to support digital transformation in education.
PENINGKATAN AKURASI KLASIFIKASI KEMATANGAN KELAPA SAWIT BERBASIS CITRA DENGAN ENSEMBLE DEEP LEARNING TEROPTIMASI DIMENSI RASIO Ahmad Rifai Ikhsanudin; Dian Ade Kurnia; Yudhistira Arie Wijaya; Dodi Solihudin; Tati Suprapti
Jurnal Mahasiswa Sistem Informasi (JMSI) Vol. 7 No. 2 (2026): Jurnal Mahasiswa Sistem Informasi (JMSI)
Publisher : Program Studi DIII Sistem Informasi - Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jmsi.v7i2.11181

Abstract

Penentuan tingkat kematangan buah kelapa sawit secara manual sering menimbulkan subjektivitas dan menurunkan efisiensi. Penelitian ini mengembangkan metode klasifikasi berbasis citra menggunakan ensemble averaging pada tiga arsitektur MobileNetV2 dengan ukuran input berbeda (224×224, 224×300, dan 300×300) untuk mengurangi varians prediksi akibat variasi dimensi dan rasio aspek citra. Dataset yang digunakan berasal dari Kaggle berjumlah 1.380 citra, dengan pembagian 80% data latih dan 20% data validasi. Proses pengolahan mencakup rescaling, aspect-ratio-aware resizing, augmentasi, serta pelatihan menggunakan transfer learning dengan optimizer Adam dan early stopping. Hasil menunjukkan bahwa model berukuran 300×300 memberikan performa terbaik dengan akurasi 95,22% dan F1-score 0,9523. Ensemble averaging menghasilkan akurasi 94,71% dan F1-score 0,9475, yang meskipun sedikit lebih rendah dari model terbaik, memberikan stabilitas prediksi yang lebih baik dibanding model individual. Temuan ini menunjukkan bahwa resolusi input yang lebih tinggi meningkatkan kualitas ekstraksi fitur, sementara ensemble averaging tetap efektif dalam mereduksi varians dan meningkatkan ketahanan sistem klasifikasi di kondisi lapangan.
The Optimization of Learning Media Through Augmented Reality to Improve Student Learning Comprehension Saeful Anwar; Tati Suprapti; Yoga Nugraha; Arif Rinaldi Dikananda
Angkasa: Jurnal Ilmiah Bidang Teknologi Vol 18, No 2 (2026): Mei
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/angkasa.v18i2.3869

Abstract

This study presents the development and evaluation of an Augmented Reality (AR) based learning media optimized to enhance students’ comprehension of camera architecture concepts. The AR system was developed using Unity 3D integrated with Vuforia SDK, implementing a marker-based AR approach to ensure stability and compatibility with limited mobile device specifications. The system architecture consists of a mobile AR client, image-marker recognition module, 3D visualization engine, and learning interaction layer designed based on multimedia learning principles and cognitive load theory. A five-stage development framework was employed: planning, material collection, assembly, implementation, and evaluation. The AR media was applied in an undergraduate informatics course involving 30 students, using a one-group pretest–posttest design. Learning outcomes were analyzed using paired t-tests, Wilcoxon tests, normalized gain, and effect size measurements. Results show significant improvements across all cognitive dimensions (p < 0.001), with very large effect sizes (dz = 3.13) and a moderate normalized gain (g = 0.42). The findings indicate that AR provides strong practical impact on higher-order cognitive skills, particularly application and analysis, while highlighting limitations related to measurement instrument validity, absence of a control group, and limited sample generalizability, which will be addressed in future research through experimental comparison and extended system performance testing.
Implementation of Deep Learning Based on Convolutional Neural Network for Detecting Images of Solar Panel Damage in Smart Grid Systems Camelia Putri Lestari; Nining Rahaningsih; Irfan Ali; Dodi Solihudin; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2225

Abstract

This study aims to implement Deep Learning based on Convolutional Neural Network (CNN) in detecting solar panel damage using thermal images as part of a Smart Grid system. The main problem addressed is the difficulty of early automatic identification of solar panel cell damage using conventional methods. Through the CNN approach, this study developed a classification model to distinguish between damaged (Defective) and undamaged (Non-Defective) solar panel conditions. The research stages included thermal image dataset collection, pre-processing, model training, and performance evaluation. The results showed that the CNN model was able to achieve an accuracy of over 87% with stable performance on the validation data. Visualization using the Grad-CAM method helps interpret the damaged areas that are the focus of the model's decision.
KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SEMBAKO MENGGUNAKAN METODE DECISION TREE Rizaldy, Farhan; Suprapti, Tati; Dwilestari, Gifthera
PELITA JURNAL PENELITIAN DAN KARYA ILMIAH Vol 25 No 2 (2025): Pelita : Jurnal Penelitian dan Karya Ilmiah [Juli - Desember]
Publisher : UNIVERSITAS ISLAM SYEKH - YUSUF TANGERANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33592/pelita.v25i2.5159

Abstract

The K-Means method is one of the Data Mining methods that is widely used in clustering research. Based on the results of research that has been conducted to build a process model for Poverty Data Clustering Analysis Using the K-Means Method Approach in Teluk Agung Village, Indramayu District, Indramayu Regency, can use RapidMiner tools by creating operators and processing parameters used for clustering the P3KE class category. The operators used in this study are Read Excel, Set Role, Select Attributes, Replace Missing Values, Nominal to Numerical, Multiply, Clustering (K-Means) and Performance operators. The operators used are 8 operators by applying the stages of Knowledge Discovery in Database (KDD). This research will apply the Davies Bouldin Index (DBI) as a way of optimising the number of clusters to group data, from the best cluster value experiment, the closest to 0 is K9 with a DBI value of -2.257, from this we can conclude that approximately 43 items from clusters 2 - 10 are included in the P3KE category, and other than the 43 items can be interpreted as still not included in the P3KE category.
Jurnal Klasifikasi KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SEMBAKO MENGGUNAKAN METODE DECISION TREE Rizaldy, Farhan; Suprapti, Tati; Dwilestari, Gifthera
PELITA JURNAL PENELITIAN DAN KARYA ILMIAH Vol 25 No 2 (2025): Pelita : Jurnal Penelitian dan Karya Ilmiah [Juli - Desember]
Publisher : UNIVERSITAS ISLAM SYEKH - YUSUF TANGERANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33592/pelita.v25i2.5162

Abstract

Poverty is one of the fundamental problems that is of concern to governments in all countries.  An important aspect to support poverty alleviation strategies is the availability of accurate and targeted poverty data. Staple food is the nine basic needs of Indonesian people, including food or drinks used in daily life. On this basis, the government often organizes basic food assistance programs for those in need. classification is one of the most commonly used prediction techniques to predict new labels or categories based on experience gained from known data. The main purpose of classification is to understand patterns or relationships between input and output variables, so that you can take appropriate decisions or actions based on the available information. Based on the results of the analysis and implementation of the Decision Tree Algorithm for classification of eligibility for basic food aid recipients in the Teluk Agung Village area, Indramayu District, Indramayu Regency, it can be concluded that the model developed has a very high level of accuracy, namely 94.83%. This model has proven effective in classifying various categories that are worthy of receiving assistance, starting from class 1, 2, 3 and not worthy of receiving assistance. The factors used in this model, such as monthly income, have been processed well through stages in the Knowledge Discovery in Databases (KDD) framework, resulting in a reliable classification. With high accuracy and performance, it is hoped that this model can be implemented practically to support decision making in mitigating the risk of non-delivery of basic food aid in the Teluk Agung Village area, Indramayu District, Indramayu Regency.
Optimalisasi Klasterisasi Tenaga Kesehatan Menggunakan K-Means dan Davies Bouldin Indexs Ayura Yufita; Rudi Kurniawan; Yudhistira Arie Wijaya; Tati Suprapti
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.96645

Abstract

Abstrak : Optimalisasi model pengelompokan data tenaga kesehatan adalah langkah strategis untuk memahami pola dan karakteristik kelompok data tertentu. Tujuan dari  penelitian ini adalah untuk  mendapatkan nilai K optimal menurut Davies Bouldin Indeks (DBI), mendapatkan nilai iterasi yang diperlukan oleh algoritma K-Means Clustering untuk mencapai hasil yang optimal, dan menentukan jenis metrik apa yang akan menghasilkan nilai (DBI) yang paling kecil. Hal ini  penting karena penelitian ini membantu perencanaan distribusi tenaga kesehatan yang lebih efisien di wilayah Jawa Barat denfan menghasilkan klaster optimal berbasis K-Means dan Optimize Parameter Grid. Penggunaan metode Knowledge Discovery in Database (KDD), yang mencakup proses pemilihan, praproses, transformasi, data mining, dan interpretasi/ evaluasi hasil. Hasil penelitian ditunjukkan pada iterasi 1-10 menggunakan K=2 dengan nilai DBI terendah sebesar 0,377.====================================================Abstract : Optimisation of health worker data clustering model is a strategic step to understand the patterns and characteristics of certain data groups. The objectives of this study are to obtain the optimal K value according to the Davies Bouldin Index (DBI), obtain the iteration value required by the K-Means Clustering algorithm to achieve optimal results, and determine what type of metric will produce the smallest (DBI) value. This is important because this research helps to plan a more efficient distribution of health workers in the West Java region by producing optimal clusters based on K-Means and Optimise Parameter Grid. The use of Knowledge Discovery in Database (KDD) method, which includes the process of selection, preprocessing, transformation, data mining, and interpretation/evaluation of results. The results showed in iterations 1-10 using K=2 with the lowest DBI value of 0.377.
Sentiment Analysis of “Cek Bansos” Application Reviews on Google Play Store Using the Naïve Bayes Algorithm NoviFirda Aini; Odi Nurdiawan; Tati Suprapti; Arif Rinaldi Dikananda; Fathurrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1883

Abstract

The rapid development of digital public services requires a deeper understanding of user perceptions and experiences regarding government applications, including Cek Bansos. This study aims to identify the polarity of user reviews by applying the Multinomial Naïve Bayes algorithm to review data collected from the Google Play Store. The methodology includes text preprocessing, sentiment labeling, feature extraction using TF–IDF, and model training and evaluation based on accuracy, precision, recall, and F1-score. The results show that the model achieves an accuracy of 79.5%, with very high performance in the negative class (recall 0.97) but poor performance in the neutral class due to data imbalance. The dominance of negative sentiment in the dataset indicates that users face significant technical difficulties, particularly in registration, verification, and service access. These findings demonstrate that Multinomial Naïve Bayes is effective as a baseline model for sentiment analysis; however, improving data balance and quality is necessary to produce a more stable, accurate, and representative model for evaluating digital public services.
Optimizing Sentiment Analysis on the Linux Desktop Using N-Gram Features Muhamad Taufiq Hidayat; Rudi Kurniawan; Tati Suprapti
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12255

Abstract

Linux, or GNU/Linux, is a widely used open-source operating system built on the Linux kernel that is available for anyone to use, known for its security and privacy advantages. With advancements in information technology, protecting privacy has become increasingly challenging due to data extraction practices done by major tech companies. This has encouraged some Mastodon users to switch to Linux, with many expressing their opinions on using Linux as their main operating system. This research seeks to analyze the sentiments of Mastodon users toward Linux through sentiment analysis to understand whether the trend is predominantly positive, negative, or neutral. The methodology used includes collecting data with the help of the Mastodon.py library which then gets manually labelled with the assistance of a linguistic expert as well as a linguistic rule proposed by previous research. The text mining process includes preprocessing steps which includes feature extraction with n-Gram to gain the most optimized result as well as employing feature selection using TF-IDF. The Naïve Bayes algorithm is employed for text classification. The entire process of data analysis is conducted with the help of AI Studio (RapidMiner) software. The results show that the highest-performing model for sentiment analysis is achieved with an n-gram value of 3, revealing user sentiment polarity towards Linux on Mastodon as follows: 42% positive, 28% negative, and 30% neutral. The sentiment analysis model has an accuracy of 63%, with a precision of 70%, recall of 80%, and an f1-score of 74% which shows that this method is able to optimize the sentiment analysis process. 
FP-Growth Algorithm for Association Model Optimization in Household Sales Data Zulfa Hana Aqliyah; Rudi Kurniawan; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.760

Abstract

This research aims to find the value of support and confidence parameters needed so that associations between products can be identified and get the value of support, confidence, lift for the association rules found, and identify products that have the highest support value in frequent itemsets. The method used is Knowledge Discovery in Databases (KDD) with the stages of data collection, data pre-processing, data transformation, data mining, dan interpretation and evaluation. Sales transaction data was collected from January 1 to September 30, 2024, focusing on support and confidence values. The results showed that the association was successfully found with a parameter value of support 0.02 and confidence 0.5. In the association found, the products SWEAT BRONZE PANTS MINI M5 and SWEAT BRONZE PANTS MINI L5 have a support value of 0.004, confidence of 0.073, and lift of 1.421. These values indicate that although the frequency of this association is low, its strength exceeds that of a random association, which can be used in marketing strategies like product bundling.The product “SENSI PEREKAT S20” has the highest support of 0.149 (14.9%. The findings provide insight into the use of data mining algorithms to design data-driven marketing strategies and more efficient inventory management.
Co-Authors Abdul Hakim Abdul Mukhyidin Achmad Fikri Achmad Suharno Adam Firmansyah Ade Irma Purnamasari Ade Irma Purnamasari Ade Rizki Rinaldi Aditia agus bahtiar Ahmad Faqih Ahmad Faqih Ahmad Muhaimin Ahmad Rifai Ikhsanudin Ai Sri Nurmala Aldi Setiawan Ali Ali Alpian Novansyah, Indi Alwan Azhar Amaliah, Novi Andi Ardiansyah Andri Yanto Apriliani, Yuni Aribah, Firyal Arif Rinaldi Dikananda ASEP SAEFUDDIN Athaullah Abrar Bayan Auliya Ayura Yufita Bani Nurhakim Beby Maryam Camelia Putri Lestari Cep Lukman Rohmat Christian Anderson Wint's II, Hans Dadang Sudrajat Darussalam, Luthvi Nurfauzi Dayanti, Resda Dian Ade Kurnia Dian Ade Kurnia Dodi Solihin Dodi Solihudin Doni Anggara Dwi Prasetyo Elsha, Dwi Fathurrohman Faujatun Hasanah Fazrian, Vivi Feri Irawan Irawan Fitri Adha Hariyati Airi Fitriani Agustina Fitriani Fitriani Gifthera Dwilestari Gifthera Dwilestari Gilang Perwati, Intan Gilang Ramadhan Gustiani Regina Pratama Putri Gustino, Gustino Habiballoh, Hafshoh Hadianti, Isan Hafshoh Habiballoh Hajaroh, Hajaroh Hartati Hartati Hendriyansyah, Hendriyansyah Hidayat, Manarul Hidayat, Muhamad Taufiq Hidayat, Peri Husni Mubarok Ilham Kurniawan Imam Arifin imam maulana, imam Indrawan, Heru Irfan Ali Irma Purnamasari, Ade Kaslani Khoirunisa, Irma Lestari, Hasanah Mahda, Muhammad Manarul Hidayat Martanto . Muhamad Basysyar, Fadhil Muhamad Taufiq Hidayat Muhammad Hilmy Naufan Mulyawan Nana Siti Nurjanah Narasati, Riri Narasati Nining Rahaningsih NoviFirda Aini Nur Amalia Nurhakim, Bani Nurmala, Sri Odi Nurdiawan Pratiwi, Intan Purnamasari, Ade Irma PUTRI EKA SARI, PUTRI EKA Raditya Danar Dana Rananda Deva Rian Raudotul Janah, Fina Rini Astuti Rini Astuti Riri Narasati Rizaldy, Farhan Rizki Ani, Fitri Rosdiana Rosdiana Rudi Kurniawan Rudi Kurniawan Rudi Kurniawan Ruli Herdiana Ryan Hmonangan Saeful Anwar Saeful Anwar, Saeful Sajidan, Dzikri Santi Nurjulaiha Shalihah, Ghina Shinta Virgiana Silalahi, Ryan H Siti Aisah, Iis siti azhar Suarna, Nana Suharno, Achmad Sukma Maula, Intan Syahputra Simbolon, Vrendi Amro Syajida, Hanna Syaripah, Imas Tegar Lazuardi, Muhammad Tengku Riza Zarzani N Tohidi, Edi Tri Aditama Tri Gustiane, Indri Umi Hayati Utami Aryanti Vinna Agustina Wahyudin, Edi Warni Ayu Hermina, Bintang Widiawati, Fitri Widisa Adi Kumara Wijaya, Yudhitira Arie Willy Prihartono Yoga Nugraha Yudhistira Arie Wijaya Yusuf Sidiq, Yusuf Sidiq Zaki Nur Rahmat Hidayat Zulfa Hana Aqliyah