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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) dCartesian: Jurnal Matematika dan Aplikasi Jurnal Sistem Komputer Proceedings of KNASTIK Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) CogITo Smart Journal INOVTEK Polbeng - Seri Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMA InfoSys MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science FINANCIAL : JURNAL AKUNTANSI Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) International Journal of Social Science Indexia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Sistem Informasi Jurnal Algoritma Jurnal Ilmiah Sains Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Eduvest - Journal of Universal Studies Jurnal INFOTEL Journal of Technology Informatics and Engineering Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Scientific Journal of Informatics CSRID INOVTEK Polbeng - Seri Informatika Jurnal DIMASTIK Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
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Universitas Kristen Satya Wacana
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Analisis Pengembangan dan Implementasi System E-learning Untuk Meningkatkan pengetahuan Agent Menggunakan Metode ADDIE Model (Study Kasus: PT.Global Infotech Solution) Nansy Stephanie Mongi; Hendry Hendry
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 2 No. 3 (2021): Mei 2021
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v2i3.2920

Abstract

The development of e-learning system ini companies is very important in the learning process and training of employees or agents to achieve company goals. Like at PT. Global Infotech solution where the learning process still uses manuals so that it hinders learning because to hold training is quite large ini incurring costs for agents who are outside the region. Therefore companies need an e-learning system to help the employee agent learning process to be more affective. E-learning system have been developed and implemented using the moodle platform, which is one of the learning management system. The development method used is the ADDIE (Analysis, Design, Implementation, Evaluation) model so that it is faster ini developing an e-learning system platform according to user needs in the company. The results of the e-learning system research were assessed through employee agent responses from the learning aspect of 84.75% with a good category, seen from the display aspect of 86.05% with the very good category, seen from the aspect of the e-learning system using the Moodle platform of 80, 32% with a good category, and the last one seen from the material aspect in the e-learning system with an interesting concept of 89.82% with the very good category. So the overall assessment of the quality of the e-learning system as a whole is 85.25 with a good category. With this e-learning system, the learning process will be more efficient in displaying learning information needed by company agents in a web-based form and can be accessed by users online as long as they are connected to the internet.
Penerapan Algoritma Support Vector Machine untuk Mendeteksi Uja-ran Kebencian dalam Media Sosial Twitter Shallom, Karsten Jonatthan; Hendry, Hendry
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7553

Abstract

Penelitian ini mengeksplorasi penerapan algoritma Support Vector Machine untuk mendeteksi ujaran kebencian di platform media sosial Twitter, khususnya dalam konteks bahasa Indonesia. Dengan lebih dari 330 juta pengguna, Twitter menjadi sarana yang rentan terhadap penyebaran ujaran kebencian yang dapat menimbulkan dampak negatif. Tujuan utama dari penelitian ini adalah mengembangkan sistem otomatis yang mampu mengidentifikasi ujaran kebencian secara efektif. Dataset yang digunakan terdiri dari 1564 tweet berbahasa Indonesia yang diambil dari isu politik pada tahun 2021. Proses analisis meliputi langkah-langkah seperti tokenisasi, stemming, dan penandaan kelas kata, diikuti dengan klasifikasi menggunakan SVM. Hasil penelitian menunjukkan bahwa 92.8% dari tweet yang dianalisis termasuk dalam kategori "no hate speech," sementara 7.2% teridentifikasi sebagai "hate speech." Model SVM menunjukkan performa yang sangat baik dengan akurasi mencapai 97.19%, recall 97.19%, presisi 97.28%, dan F1 Score 96.82%, tanpa adanya False Negatives. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam menciptakan lingkungan online yang lebih aman dan positif, serta meningkatkan pemahaman tentang karakteristik bahasa Indonesia dalam konteks deteksi ujaran kebencian.
MEMPREDIKSI TINGKAT KECELAKAN JALAN RAYA DI SALATIGA MENGGUNAKAN MACHINE LEARNING Febrian, Andika Rossy; Hendry, Hendry
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6959

Abstract

Kecelakaan merupakan suatu kejadian dijalan yang tidak terduga yang melibatkan kendaraan dan mengakibatkan korban jiwa maupun kerugian material, kecelakaan juga disebut sebagai kejadian yang memiliki multi faktor atau memiliki banyak penyebab yang mempengaruhi terjadinya kecelakaan. Penelitian ini memiliki tujuan yaitu untuk meramalkan atau memprediksi kecelakaan lalu lintas menggunakan metode Random Forest dan Linear Reggresion dengan bahasa pemrograman Python. Data yang digunakan dalam penelitian ini yaitu data dari BPS (Badan pusat Statistik) kota Salatiga dengan periode dari Tahun 2017 sampai dengan Tahun 2023 dengan selang waktu perbulan. Penerapan dari kedua model menunjukan untuk model Linear Reggression memiliki hasil yang lebih baik dari pada model Random Forest ini dikarenakan pada perhitungan Random Forest selalu menunjukan hasil decimal dan tidak genap akan tetapi kekurangan tersebut dapat dibenahi dengan menggunakan tools Difference yang beeguna untuk membantu mengidentifikasi pola atau kesalahan system dan memberikan arahan untuk perbaikan kedepannya
Acceptance and Use of the MyASN Application Among Civil Servants: An Integration of TAM and IS Success Model in South Central Timor Regency Nifu, Merlyn Gizella; Sulistyo, Wiwin; Hendry, Hendry
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2639

Abstract

The MyASN application is a mobile-based mandatory system designed to support Civil Servants (ASN) in accessing personnel-related information. However, its implementation still faces challenges, including data inconsistencies and system integration issues. This study analyzes the factors influencing the acceptance and actual use of the MyASN application among civil servants in South Central Timor Regency. The research integrates the Technology Acceptance Model (TAM) and the DeLone and McLean Information Systems Success Model. A quantitative survey involved 250 respondents, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS. The results reveal a distinct adoption pattern for mandatory government systems. Service quality has no significant impact on user perceptions, indicating that users prioritize independent system functionality over technical support. System quality acts as a baseline expectation that significantly enhances perceived ease of use and perceived usefulness, but it does not significantly influence user satisfaction. Conversely, information quality emerges as the application's true core value; while it does not affect ease of use, it strongly drives perceived usefulness and user satisfaction. Furthermore, user satisfaction acts as the strongest predictor of users’ intention to continue using the application, which directly drives actual system use. Practically, these findings recommend that the National Civil Service Agency (BKN) and regional governments prioritize data accuracy to achieve user satisfaction and maintain system stability to prevent user dissatisfaction, rather than solely focusing on support services.
Application of the PIECES Framework Method in E-Report Evaluation Muhammad Sholikin; Eko Sediyono; Hendry Hendry
Jurnal Penelitian Pendidikan IPA Vol 9 No SpecialIssue (2023): UNRAM journals and research based on science education, science applic
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9iSpecialIssue.6028

Abstract

The evaluation of the E-report application at SMK N 1 Banyudono is integral for assessing its alignment with the latest curriculum guidelines and its effectiveness in meeting students' learning and assessment needs. This evaluation ensures that learning support technologies, such as E-report, stay relevant, responsive to user needs, and supportive of the school's long-term educational objectives. Using the PIECES method, the assessment of user satisfaction comprehensively considers Performance, Information, Economy, Control, Efficiency, and Services indicators. The Likert scale aids in gauging user satisfaction across various aspects. Quantitative data analysis involves 26 respondents, including teachers, homeroom teachers, curriculum staff, and school principals, providing a holistic perspective on learning and assessments management. The PIECES method and Likert scale evaluation reveal a satisfaction level of 4.52, categorizing it as "SATISFIED." This comprehensive assessment covers system performance, information accuracy, cost-effectiveness, system control ease, time efficiency, and service flexibility. The qualitative descriptive approach using the PIECES method and Likert scale identifies areas for improvement, ensuring the E-report application remains efficient, relevant, and aligned with DITJEN VOKASI guidelines
Prediksi kebangkrutan perusahaan menggunakan metode klasifikasi: Studi kasus pada industri Ibrahim Ibrahim; Hendry Hendry
AITI Vol 23 No 2 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i2.305-318

Abstract

Prediksi kebangkrutan perusahaan merupakan aspek penting dalam bidang keuangan karena dapat memberikan dampak signifikan terhadap investor, kreditur, manajemen perusahaan, serta pemangku kepentingan lainnya dalam pengambilan keputusan strategis. Penelitian ini bertujuan untuk mengembangkan model prediksi kebangkrutan perusahaan yang akurat menggunakan algoritma XGBoost yang dioptimalkan melalui proses penyesuaian hiperparameter (hyperparameter tuning). Selain itu, penelitian ini juga menerapkan metode Synthetic Minority Over-sampling Technique (SMOTE) untuk mengatasi tantangan ketidakseimbangan data dengan meningkatkan representasi kelas minoritas. Model yang dikembangkan diuji pada dua dataset berskala besar (Taiwan dan US) untuk mengevaluasi konsistensi kinerja serta kemampuan generalisasi model. Hasil penelitian menunjukkan bahwa model XGBoost dengan hyperparameter tuning mampu menghasilkan performa terbaik dengan tingkat akurasi sebesar 98,94%, precision sebesar 0,98, dan recall sebesar 1,0. Selain itu, hasil pengujian menunjukkan bahwa model tersebut memiliki kinerja yang konsisten tanpa indikasi overfitting. Hasil pendekatan ini membuktikan bahwa XGBoost dengan penyesuaian hiperparameter mampu memberikan prediksi kebangkrutan yang akurat, konsisten, dan dapat diandalkan untuk diterapkan dalam berbagai konteks industri serta pada skala penggunaan yang lebih luas.
Analisis Tingkat Kepuasan Siswa terhadap Program Makan Bergizi Gratis di SMA Negeri 1 Manokwari dengan Pendekatan Mining Titin Restiani Mendrofa; Hendry Hendry
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 2 (2026): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i2.1842

Abstract

The Free Nutritious Meal Program (MBG) aims to improve students' nutritional status and learning quality; therefore, a data-driven evaluation is required to assess its implementation effectiveness. This study seeks to analyze the influence of program implementation on student satisfaction and to identify the resulting satisfaction segments. The research employed a descriptive quantitative approach involving 499 respondents. Data were collected through a Likert-scale questionnaire covering variables of program implementation and student satisfaction. The analysis utilized MANOVA, K-Means Clustering, and one-way ANOVA. The MANOVA results indicated that all implementation variables significantly affected student satisfaction (Sig < 0.001), with compliance to nutritional standards emerging as the most dominant factor (Wilks' Lambda = 0.805; F = 23.634). The K-Means analysis produced three satisfaction clusters: high (314 students), moderate (172 students), and low (13 students). The ANOVA test confirmed significant differences among clusters (Sig < 0.001), with the availability of healthy food (F = 145.428) as the main distinguishing factor. The findings of this study indicate that food variety and the availability of healthy meals are the main factors distinguishing students' satisfaction levels; therefore, the MBG program should focus on improving these two aspects to enhance overall student satisfaction.
Peningkatan Knowledge Capture dan Knowledge Sharing dalam KMS Tools dengan Kaizen Form Faisal Hakim Amrullah; Hendry; Irwan Sembiring
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3409

Abstract

This study discusses the improvement of knowledge capture and knowledge sharing through the strengthening of a Kaizen-based Knowledge Management System (KMS) in the footwear manufacturing industry. The main problems include the suboptimal management of tacit knowledge and the limitations of document search based on simple keywords. This study applies an information retrieval method using TF-IDF and Cosine Similarity on 800 validated Kaizen documents through preprocessing, weighting, and document similarity measurement stages. The test results show that the proposed method performs better than conventional keyword-based search, with a precision value of 0.60, recall of 0.75, and F1-score of 0.67. The contribution of this study lies in the application of information retrieval methods to improve the effectiveness of knowledge retrieval in a Kaizen-based KMS, thereby supporting continuous improvement and organizational learning.
Co-Authors Ade Iriani Adenia Kusuma Dayanthi Adriyanto Juliastomo Gundo Agista Nindy Yuliarina Aldi Lasso Anton Hermawan Anugerah Widi April Lia Hananto Atik Setyanti, Angela Aviv Yuniar Rahman Baihaqi, Kiki Ahmad Benedictus Lanang Ido Hernanto Christine Dewi Daniel D. Kameo Danny Manongga Danny Manongga Darmawan Utomo Darwin Lie Dewasasmita, Elsha Yuandini Dewi Puspitasari Eko Sediyono eric secada purba Erick Alfons Lisangan Erits Talapessy Erwien Christianto Ester Caroline Dwi Wijaya Wijaya Faisal Hakim Amrullah Fauzi Ahmad Muda Febrian, Andika Rossy Franly Salmon Pattiiha Fredryc Joshua Pa&#039;o Fredryc Joshua Pa'o Giarti, Giarti Gunawan, Ricardho Handoko, Andrew C Hanita Yulia Hendra Waskita Herdin Yohnes Madawara Hindriyanto Dwi Purnomo Huda, Baenil Ibrahim Ibrahim Irwan Sembiring Ismael Ismael Ivan Sukma Hanindria Ivanna K. Timotius Iwan Setiawan Iwan Setyawan Jessica Margaret Br Sembiring Joko Siswanto Julians, Adhe Ronny Kesumawati, Ramadini Kevin Fransisco Kho, Delvian Christoper Krismiyati Kristoko Dwi Hartomo Kurniawan Teguh Martono Leni Marlina Lidia Gayatri Madawara, Herdin Yohnes Mado, Priscianus Mikael Kia Magda Kitty Hartono Mahulete, Ebenhaezer Yohanes Abdeel Manongga, Daniel Margaretha Intan Pratiwi Hant Martaliana Putri Agustina Merryana Lestari Muhammad Rizky Pribadi Muhammad Sholikin Nadia Sofie Soraya Nalbraint Wattimena Nansy Stephanie Mongi Nifu, Merlyn Gizella Nugraha, Febrina Tesalonika Panja, Eben Paryono, Tukino Pratama Siregar, Hari Nanda Pratama, Arya Damar Purnomo, Hendryanto Dwi Ramos Somya Ravensca Matatula Ravensca Matatula Richard V. Llewelyn Robertus Bagaskara Radite Putra Ronny Julians, Adhe Rostina, Cut Fitri Rung Ching Chen Santoso, Joseph Teguh Saputri, Adelliya Dewi Septhiani, Angeline Shallom, Karsten Jonatthan Simanjuntak, Dahnil Anzar Suharyadi Suherman, Suherman Sutarto Wijono Suvirocana, Suvirocana Syefudin Syefudin Teddy Marcus Zakaria Thea Thiranadya Mardita Bulamey Theophilus Wellem Theopillus J. H. Wellem Titin Restiani Mendrofa Tukino, Tukino Uly, Novem Untung Rahardja Wahyuningsih, Novia Wibowo, Kurniawan Indra Winny purbaratri Winsy C.D Weku Wiwin Sulistyo Yessica Nataliani Yessica Nataliani