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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Informatika KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Informasi dan Komputer Indonesian Journal of Applied Informatics JUTIS : Jurnal Teknik Informatika 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 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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ALGORITMA NAIVE BAYES UNTUK MENINGKATKAN MODEL KLASIFIKASI PENERIMA PROGRAM INDONESIA PINTAR DI SDN 2 PURWAWINANGUN Darussalam, Luthvi Nurfauzi; Kurniawan, Rudi; Wijaya, Yudhistira Arie; Suprapti, Tati
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5882

Abstract

Penelitian ini bertujuan meningkatkan keakuratan klasifikasi penerima Program Indonesia Pintar (PIP) di SDN 2 Purwawinangun, Kabupaten Kuningan. Metode yang lambat dan kurang akurat digantikan dengan algoritma Naive Bayes untuk menganalisis data siswa berdasarkan kriteria tertentu. Proses penelitian meliputi pengumpulan data sekunder, preprocessing data, dan implementasi algoritma Naive Bayes. Hasilnya, model ini mencapai akurasi 96,47% dalam menentukan kelayakan penerima PIP, dengan mempertimbangkan atribut seperti latar belakang sosial ekonomi dan kinerja akademik siswa. Temuan menunjukkan bahwa algoritma ini efisien dalam mengolah dataset kompleks dibandingkan metode manual. Namun, kinerja model sangat bergantung pada kualitas data awal, sehingga data yang tidak lengkap dapat mempengaruhi hasil.Penelitian merekomendasikan penerapan metode ini di sekolah lain dan integrasi algoritma tambahan, seperti Decision Tree, untuk validasi hasil. Dengan pendekatan ini, seleksi penerima PIP menjadi lebih tepat sasaran, efisien, dan transparan.
PENERAPAN MODEL EXPERIENTAL LEARNING MELALUI METODE DISKUSI PADA MATERI PENGELOLAAN KEUANGAN SEDERHANA UNTUK MENINGKATKAN HASIL BELAJAR SISWA KELAS XII MANAJEMEN PERKANTORAN1 SMK NEGERI 1 TASIKMALAYA Suprapti, Tati
Jurnal Inovasi dan Teknologi Pendidikan Vol. 2 No. 3 (2024): Jurnal Inovasi dan Teknologi Pendidikan
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/jurinotep.v2i3.76

Abstract

The problem in this research is whether the application of the Experiential Learning Model through the Discussion Method can improve the learning outcomes of class XII MP1 students at SMK Negeri 1 Tasikmaya. The aim of the research is to increase the effectiveness of implementing the Experiential Learning Model through the Discussion Method in an effort to improve learning outcomes for Simple Financial Management Material. Perpetual Assessment Method for class The subjects in this research were 35 students of Class XII Office Management1 at SMK Negeri 1 Tasikmalaya for the 2022/2023 academic year. Student learning effectiveness is measured based on individual learning completeness criteria. The results of the research show that through the application of the Experiential Learning Model through the Discussion Method it can increase student activity and competence in simple Financial Management subjects. . This can be seen from: (1) there is an increase in student activity in each cycle. Student activity in cycle I was 37.13%, and cycle II was 76.16%; (2) there is an increase in class averages and student learning completeness. The class average in cycle I was 80 and cycle II was 87. Student learning completeness as measured by the cognitive competency test in cycle I was 66%, and cycle II was 91.62%. The conclusion from the research results regarding the Application of the Experiential Learning Model Through the Discussion Method in Class XII Office Management 1 SMK Negeri 1 Tasikmalaya was declared successful
Peningkatan Pengetahuan Masyarakat tentang Pemanfaatan Seledri (Apium graveolens L.) sebagai Antimikroba Sari, Putri Eka; Suprapti, Tati; Elsha, Dwi
Jurnal Pengabdian kepada Masyarakat Wahana Usada Vol. 7 No. 2 (2025): Desember: Jurnal Pengabdian kepada Masyarakat Wahana Usada
Publisher : Sekolah Tinggi Ilmu Kesehatan KESDAM IX/Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47859/wuj.v7i2.744

Abstract

Infections caused by pathogenic microorganisms remain a major health challenge. One preventive promotive effort to reduce infection incidence is through the use of traditional medicinal plants, such as celery (Apium graveolens L.) which is known to have antimicrobial activity. This community service program aimed to increase the knowledge of PKK cadres in Klender Urban Village regarding the antimicrobial benefits of celery through educational outreach. The activity was held on June 12, 2025, involving 14 PKK cadres as participants. Evaluation was carried out using pre-test and post-test questionnaires, analyzed using the Wilcoxon test. The results showed a significance value of 0.002 (p<0.05), indicating a significant increase in knowledge after the intervention. The average percentage of correct answer increased by 37.2%. Additionally, all of participants demonstrated good knowledge levels in the post-test and none were in the poor category. These findings indicate that education through outreach is effective in improving community understanding of celery’s antimicrobial potential. Local plant-based education is expected to be a sustainable preventive strategy to improve public health.Keywords: education, celery, antimicrobial, knowledge PKK cadres
Evaluasi Prediksi Harga Saham Nokia Menggunakan LSTM Univariat dengan Pendekatan Walk-Forward Validation Saputra, Roni; Martanto, Martanto; Dana, Raditya dana; Solihudin, Dodi; Suprapti, Tati
JURNAL TEKNIK INFORMATIKA UNIS Vol. 13 No. 2 (2025): Jutis (Jurnal Teknik Informatika)
Publisher : Universitas Islam Syekh Yusuf

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

Abstract

Prediksi harga saham merupakan tantangan sentral di pasar modal yang kompleks dan volatil.Meskipun model pembelajaran mendalam seperti Long Short-Term Memory (LSTM) telahmenunjukkan potensi, banyak penelitian mengabaikan masalah multikolinearitas pada modelmultivariat dan menggunakan metode evaluasi yang tidak realistis. Untuk mengatasi ini,penelitian ini mengembangkan model prediksi harga saham Nokia menggunakan arsitekturLSTM univariat yang hanya memanfaatkan harga penutupan, sebuah keputusan yangdidasarkan pada bukti empiris multikolinearitas tinggi antar fitur harga. Kinerja modeldievaluasi secara ketat menggunakan Walk-Forward Validation (WFV) untuk mensimulasikankondisi perdagangan nyata dan menghindari bias evaluasi. Hasilnya menunjukkan performayang sangat baik dan stabil, dengan model mampu menjelaskan 94.46% varians data (R² =0.9446) dan mencapai Mean Absolute Percentage Error (MAPE) sebesar 2.75%. Konsistensiini terbukti melalui 30 iterasi WFV, yang mengonfirmasi ketahanan model di berbagai kondisipasar. Penelitian ini menyimpulkan bahwa pendekatan model LSTM univariat yang dievaluasidengan WFV terbukti efektif dan andal, bahkan dapat menjadi pilihan superior dibandingkanmodel yang lebih kompleks. Temuan ini menegaskan bahwa relevansi fitur dan standarevaluasi yang ketat lebih krusial daripada kompleksitas arsitektur, memberikan kontribusimetodologis penting bagi pengembangan model prediksi finansial yang andal di masa depan.
Klasifikasi Telur Fertil dan Infertil Berbasis Hybrid MobileNetV3 dengan Mekanisme Attention dan Texture Fusion Bani Nurhakim; Dadang Sudrajat; Tati Suprapti; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

Accurate fertile-infertile egg classification is crucial to improve hatching productivity and sorting efficiency. This study proposes MobileFusionV3, a MobileNetV3 architecture enriched with CBAM (Convolutional Block Attention Module) and Hybrid Texture Fusion (LBP and GLCM) to combine deep and texture features to be more robust to candling illumination variations. A dataset of 1,275 candling images (675 fertile, 600 infertile) was subjected to preprocessing (resizing, normalization, background enhancement) and realistic data augmentation (rotation, brightness/contrast changes, Gaussian noise, illumination variations). The model was trained using transfer learning, early stopping, and an evaluation scheme based on accuracy, precision, recall, F1-score, and AUC. The test results showed an accuracy of 97.2%, precision of 96.8%, recall of 97.5%, F1 of 97.1%, and AUC of 0.99, surpassing previous designs that did not use attention mechanisms and texture fusion. Grad-CAM++ analysis confirms the model's focus on physiologically relevant regions (embryonic shadow and air-cell), thus improving the reliability of interpretation. These findings indicate that lightweight, efficient designs based on attention and texture fusion have the potential to be implemented in smart hatchery systems and edge/mobile devices while maintaining high accuracy.
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 Aini, NoviFirda; Nurdiawan, Odi; Suprapti, Tati; Dikananda, Arif Rinaldi; 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.
OPTIMASI NILAI DAVIES BOULDIN INDEX PADA PROGRAM PENDAFTARAN TANAH SISTEMATIS LENGKAP (PTSL) MENGGUNAKAN ALGORITMA K-MEANS DAN PCA Muhammad Hilmy Naufan; Rudi Kurniawan; Tati Suprapti
E-Link: Jurnal Teknik Elektro dan Informatika Vol. 20 No. 1: Mei 2025
Publisher : Universitas Muhammadiyah Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30587/e-link.v20i1.9063

Abstract

Penelitian ini bertujuan mengoptimalkan proses clustering data program Pendaftaran Tanah Sistematis Lengkap (PTSL) dengan mengimplementasikan algoritma K-Means yang dikombinasikan dengan Principal Component Analysis (PCA) dan mengevaluasi hasilnya menggunakan Davies Bouldin Index (DBI). Dalam metode penelitian yang diterapkan meliputi pengumpulan data dari Desa Bandorasawetan, Kecamatan Cilimus, Kabupaten Kuningan, pemilihan data, transformasi data, data mining, dan interpretasi/evaluasi. Hasil penelitian menunjukkan bahwa klaster yang optimal dicapai pada K = 5 dengan pendekatan Fixed Number menggunakan 1 Number of Components yang mempertahankan atribut NJOP Bangunan. Atribut ini memiliki distribusi yang lebih terpusat dalam satu cluster dalam arti memiliki pola yang konsisten, dengan nilai DBI sebesar 0.049, memiliki kinerja lebih baik dibandingkan K-Means tanpa PCA dengan DBI mencapai 0.466. Dari total 5 klaster yang terbentuk, cluster terbaik yang teridentifikasi berdasarkan hasil selisih rata-rata antara avg. within centroid distance dan avg. within centroid distance_cluster yaitu cluster 0, karena memiliki jarak terdekat dengan komponen utama (PC1) sebesar 128918767.1. Studi ini diharapkan dapat meningkatkan kualitas dalam pengelolaan data pertanahan agar menjadi lebih efektif.
Optimizing Sentiment Analysis on the Linux Desktop Using N-Gram Features Hidayat, Muhamad Taufiq; Kurniawan, Rudi; Suprapti, Tati
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. 
Co-Authors Abdul Hakim Abdul Mukhyidin Abrar Bayan, Athaullah Achmad Suharno Adam Firmansyah Ade Irma Purnamasari Ade Irma Purnamasari Ade Rizki Rinaldi Aditia agus bahtiar Ahmad Faqih Ahmad Faqih Aini, NoviFirda Aldi Setiawan Ali Ali Alpian Novansyah, Indi Amaliah, Novi Andi Ardiansyah Andri Yanto Apriliani, Yuni Aribah, Firyal Arif Rinaldi Dikananda ASEP SAEFUDDIN Auliya Ayura Yufita Azhar, Alwan Bani Nurhakim Cep Lukman Rohmat Christian Anderson Wint's II, Hans Dadang Sudrajat Dana, Raditya dana Darussalam, Luthvi Nurfauzi Dayanti, Resda Dian Ade Kurnia Dikananda, Arif Rinaldi Dodi Solihin Doni Anggara Dwi Prasetyo Elsha, Dwi Fathurrohman, Fathurrohman Faujatun Hasanah Fazrian, Vivi Feri Irawan Irawan Fikri, Achmad 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 Hayati, Umi 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 Lukman Rohmat, Cep Mahda, Muhammad Manarul Hidayat Martanto . Maryam, Beby Muhaimin, Ahmad Muhamad Basysyar, Fadhil Muhammad Hilmy Naufan Mulyawan Nana Siti Nurjanah Narasati, Riri Narasati Nining Rahaningsih 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 Rizki Ani, Fitri Roni Saputra, Roni 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 Solihudin, Dodi Sri Nurmala, Ai 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 Umi Hayati Utami Aryanti Vinna Agustina Wahyudin, Edi Warni Ayu Hermina, Bintang Widiawati, Fitri Widisa Adi Kumara Wijaya, Yudhitira Arie Willy Prihartono Yudhistira Arie Wijaya Yusuf Sidiq, Yusuf Sidiq Zaki Nur Rahmat Hidayat Zulfa Hana Aqliyah