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All Journal E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Jurnal Penelitian Pendidikan IPA (JPPIPA) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ekonomi, Manajemen, dan Akuntansi Universitas Ngudi Waluyo Majalah Ilmiah Dian Ilmu Jurnal Pengabdian Masyarakat (ABDIRA) JUSTIAN - Jurnal Sistem Informasi Akuntansi ProBisnis : Jurnal Manajemen Jurnal Algoritma The International Conference on Education, Social Sciences and Technology (ICESST) Indonesian Journal of Midwifery (IJM) "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika AMMA : Jurnal Pengabdian Masyarakat Jurnal Ar Ro'is Mandalika (Armada) International Journal of Economics and Management Sciences Jurnal Bakti Humaniora Jurnal Informatika dan Kesehatan INOVTEK Polbeng - Seri Informatika Management Dynamics: International Journal of Management and Digital Sciences JHE (Journal of Health Education)
Kustiyono
Universitas Ngudi Waluyo
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Penyuluhan Inovatif: Talent Management dan AI Digital Brainstorming sebagai Upaya Pembentukan Kepribadian Unggul Peserta Didik MA Darul Ma’arif Pringapus Satria Avianda Nurcahyo; Ahmad Ali; Ucta Pradema Sanjaya; Priyanto Priyanto; Teguh Harso Widagdo; Kustiyono Kustiyono; Noor Laila Ramadhani; Pipit Sundari; Yeni Indraningtyas
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3154

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterampilan peserta didik MA Darul Ma’arif Pringapus dalam mengelola potensi diri melalui penerapan Talent Management dan teknologi AI Digital Brainstorming sebagai sarana pembentukan kepribadian unggul. Latar belakang kegiatan ini berangkat dari tantangan dunia pendidikan modern yang menuntut generasi muda untuk memiliki kepribadian adaptif, kreatif, dan berdaya saing di era digital. Metode pelaksanaan meliputi penyuluhan interaktif, simulasi penggunaan AI brainstorming tools, serta pendampingan personal dalam mengidentifikasi bakat dan minat peserta didik. Hasil kegiatan menunjukkan peningkatan signifikan dalam kemampuan peserta memahami konsep talent mapping, berpikir kritis, serta mengoptimalkan teknologi digital untuk pengembangan diri. Peserta didik juga menunjukkan antusiasme tinggi terhadap penerapan teknologi kecerdasan buatan sebagai sarana eksplorasi ide dan pemecahan masalah secara inovatif. Kegiatan ini memberikan dampak positif terhadap pembentukan karakter mandiri, percaya diri, serta tangguh menghadapi perubahan. Dengan demikian, penyuluhan inovatif berbasis Talent Management dan AI Digital Brainstorming menjadi langkah strategis dalam menyiapkan peserta didik berkepribadian unggul di lingkungan pendidikan madrasah.Kata kunci: penyuluhan inovatif, talent management, AI digital brainstorming, kepribadian unggul, peserta didik
Analisis Komparatif Naive Bayes dan K-Nearest Neighbor dalam Klasifikasi Kinerja Produk Marketplace Berbasis Data Mining dengan Performa Optimal pada Naive Bayes Lucyana Desy Anggraeni; Kustiyono
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.23-1.3300

Abstract

Product performance classification in marketplaces is a challenge in data analysis because it involves heterogeneous numerical and categorical attributes and an uneven class distribution. This study compares the performance of the Naive Bayes (NB) and K-Nearest Neighbor (KNN) algorithms using a data mining approach on 1,200 data points that have undergone pre-processing. The evaluation was conducted using a cross-validation scheme with accuracy, kappa, precision, and recall metrics. The test results showed that NB was significantly superior with an average accuracy of 80.10% ± 0.61%, while KNN only reached 43.85% ± 1.36%. The kappa, precision, and recall values also showed the consistency of NB's superiority in capturing class distribution patterns. These findings confirm that the probabilistic approach is more effective than distance-based methods that are sensitive to data overlap and feature distribution. Theoretically, this study confirms the importance of algorithm suitability with data structure characteristics in determining classification performance in the marketplace context.
Kombinasi Decision Tree dan Naïve Bayes dengan Explainable AI untuk Prediksi Dropout Agung Wibowo; Kustiyono; Eko Nur Hermansyah
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.23-1.3367

Abstract

Predicting student dropout risk is crucial for supporting early intervention and accountable academic decision-making. This study proposes a multi-class classification (Dropout, Enrolled, Graduate) using voting (Naïve Bayes and Decision Tree) and Explainable AI to enhance transparency. The dataset consists of 4,424 records with 36 features. Evaluation was conducted using k-fold stratified cross-validation (k=10) and the F1-macro metric. The results show that model performance is relatively close and stable at k=10, so model selection must consider the trade-off between performance and interpretability. The main contribution of this research is a web-based early warning DSS prototype that integrates Voting (NB+DT) with an XAI module (SHAP–LIME) so that predictions can be explained, audited, and followed up with academic intervention recommendations.
Dorongan Terhadap Upaya Kearifan Lokal dalam Memengaruhi Keputusan Investasi Digital Didasarkan Pada Literasi Keuangan Digital dan Financial Technology Yeni Indraningtyas; Kustiyono; Pipit Sundari; Ari Siswati; Satria Avianda Nurcahyo; Ahmad Ali
Majalah Ilmiah DIAN ILMU Vol 25 No 2 (2026): MAJALAH ILMIAH "DIAN ILMU" APRIL 2026
Publisher : Sekolah Tinggi Ilmu Administrasi (STIA) Pembangunan Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37849/midi.v25i2.503

Abstract

Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis faktor-faktor yang mempengaruhi keputusan investasi digital, tinjauan literasi keuangan digital, dan teknologi keuangan yang dimediasi oleh kearifan lokal. Selain itu, penelitian ini menyajikan model dasar baru dalam upaya mengisi ruang terbatas pada isu keputusan investasi digital dari penelitian sebelumnya. Secara umum, keputusan investasi dipengaruhi melalui pendekatan rasional, dalam penelitian ini unsur-unsur budaya filosofis Jawa " nastiti dan ati-ati " diuji untuk mengetahui dampaknya terhadap pengambilan keputusan investasi digital. Penelitian ini bertujuan untuk menguji secara empiris penentuan faktor-faktor yang mempengaruhi keputusan investasi digital dalam tinjauan literasi keuangan digital dan teknologi keuangan yang dimediasi oleh kearifan lokal. Selain itu, penelitian ini menyajikan model dasar baru dalam upaya mengisi ruang terbatas pada isu keputusan investasi digital dari penelitian yang telah dilakukan sebelumnya. Metode kuantitatif dipilih dalam penelitian ini untuk mewakili fenomena keputusan investasi keuangan digital Generasi X, Y dan Z di Indonesia. Data dalam penelitian ini diperoleh melalui penyebaran kuesioner dan dikumpulkan, total 207 responden dengan teknik pengambilan sampel acak sederhana dan snowball. Berdasarkan temuan penelitian ini, terlihat bahwa terdapat pengaruh langsung financial technology dan kearifan lokal terhadap keputusan investasi digital, sedangkan literasi keuangan digital tidak berpengaruh terhadap keputusan investasi digital. Pengaruh tidak langsung ditunjukkan pada variabel literasi keuangan dan financial technology melalui kearifan lokal terhadap keputusan investasi digital.  
HEGEMONI TEKNOLOGI DALAM PENDIDIKAN MENANTANG BATAS KONSTITUSIONAL BAGI SISWA SEKOLAH MENENGAH ATAS NEGERI ( SMAN) 2 UNGARAN Muhamad Latif; Kustiyono
Jurnal Bakti Humaniora Vol. 6 No. 1 (2026): JUNI 2026
Publisher : Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35473/jbh.v6i1.5210

Abstract

Kegiatan Pengabdian kepada Masyarakat ini dilatarbelakangi oleh fenomena hegemoni teknologi di Sekolah Menengah Atas Negeri (SMAN) 2 Ungaran yang berpotensi mengikis kedaulatan diri dan hak-hak konstitusional siswa di tengah masifnya digitalisasi pendidikan. Penggunaan platform digital dan algoritma personalisasi sering kali dilakukan tanpa pemahaman mendalam mengenai perlindungan privasi dan kebebasan berekspresi, sehingga menciptakan kerentanan hukum bagi siswa. Tujuan dari pengabdian ini adalah untuk mengevaluasi implementasi teknologi pendidikan agar tetap sejalan dengan prinsip-prinsip konstitusional melalui pemberdayaan civitas akademika. Metode pelaksanaan dimulai dengan analisis kebutuhan awal melalui wawancara mendalam, dilanjutkan dengan pengumpulan data melalui kuesioner dan observasi partisipatif untuk mengukur tingkat kesadaran hak digital siswa. Data yang terkumpul dianalisis secara kualitatif dan kuantitatif untuk memetakan tantangan konstitusional yang dihadapi. Hasil dari kegiatan ini menunjukkan adanya pola kerentanan hak siswa yang kemudian ditindaklanjuti dengan perumusan rekomendasi kebijakan praktis. Simpulan dari pengabdian ini adalah dihasilkannya panduan strategis yang dipresentasikan melalui lokakarya untuk memastikan inovasi teknologi di sekolah tetap menjamin kedaulatan, keamanan, dan keadilan bagi siswa sesuai amanat konstitusi.
Sistem Manajemen Order dan Pencatatan Keuangan Berbasis Website untuk Layanan Jasa Produk Digital di Startup Irvan Pratama Putra; Kustiyono
Jurnal Sistem Informasi Akuntansi Vol 7 No 1 (2026): : Periode Maret 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v7i1.12194

Abstract

Digital product services in startups and small businesses still face many problems in order management and financial recording, which are carried out manually or using separate applications. This reduces work efficiency and increases the risk of recording errors and difficulties in monitoring order and financial status. This study aims to create an integrated web-based digital order management and recording system to assist in the management of digital product services. The research methods used were observation and interviews to analyze system requirements, while the system development method used the Waterfall model of the Software Development Life Cycle (SDLC). System testing was conducted using the black box testing method to ensure that all functions ran according to requirements. The results of the study show that the developed system is capable of improving the efficiency of order management and financial recording processes, as well as increasing data accuracy by minimizing recording errors and data duplication. The integration of order and financial data makes it easier for business owners to monitor work progress and financial conditions in real-time. Thus, this system can be an effective solution in supporting the management of digital product service businesses in a more structured and integrated manner.
a Sentiment Analysis of Free Meal Plans on Social Media using Naïve Bayes Algorithms Yoga Zaen Vebrian; Kustiyono
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/3m2fcz69

Abstract

This study analyses public sentiment towards the "Free Meal Plan" initiative introduced by the political pair Prabowo-Gibran. This policy aims to assist underprivileged communities in Indonesia and is a significant issue in the social and political context. Data was collected from the social media platform X (formerly Twitter), gathering 501 relevant comments based on their connection to the topic and high levels of engagement (such as retweets and likes). The comments were then processed using Text Preprocessing and TF-IDF techniques and applied to a Naïve Bayes model. The model achieved an accuracy of 69.3%, a precision of 72%, a recall of 57.05%, and an F1 score of 54.5%. These results indicate that the model is capable of classifying public sentiment, though it has challenges in accurately detecting negative sentiment. These findings provide valuable insights for policymakers to design more effective communication and policy strategies, particularly in addressing criticism or public dissatisfaction. The study highlights the importance of using text processing and machine learning techniques to analyze social media data in a structured way.
Customer Data Management For Citynet Using Geolocation-Based Internet Broadband Registration Form Application Alfiano Aldo Pamungkas; Kustiyono
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/60hw1d67

Abstract

This study aims to address the inefficiencies in CityNet's customer data management by developing a geolocation-based registration form application. Currently, CityNet relies on Google Forms for data collection and WhatsApp for location sharing, leading to inaccuracies, inefficiencies, and delays in the installation process. The proposed system integrates geolocation to automatically capture customer locations, reducing input errors and streamlining data processing. This research follows the waterfall development model, encompassing needs analysis, system design, implementation, and usability testing involving CityNet administrators and customers. The results indicate an 80% reduction in input errors and a 30% improvement in operational efficiency. Additionally, the system seamlessly integrates with Google Spreadsheet, Telegram, and email, ensuring real-time data synchronisation and faster response times. While the application significantly enhances CityNet's operational workflow, challenges such as user adoption and dependency on internet connectivity remain. This study provides a scalable solution for broadband providers seeking efficient customer data management with location-based automation.
Naïve Bayes Berbasis TF-IDF Meningkatkan Kinerja Klasifikasi Berita Hoaks Program Makan Bergizi Gratis Putri Wulandari; Kustiyono
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.23-1.3484

Abstract

The rapid advancement of information technology has led to an increase in the spread of fake news in digital media, which has the potential to influence public opinion; therefore, an automated system is needed to distinguish between fake news and facts. This study aims to classify fake news and facts using a text mining approach with the TF-IDF method for feature extraction and the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms as classification methods. The dataset consists of 1,121 news items obtained from text sources, which underwent preprocessing, word weighting using TF-IDF, and handling of data imbalance using the Synthetic Minority Oversampling Technique (SMOTE), applied to the training data during the cross-validation process to prevent data leakage. Model evaluation was conducted using the metrics accuracy, precision, recall, and F1-score. The results of the study show that the Naïve Bayes algorithm outperforms KNN with an accuracy of 95.74%, precision of 94.98%, recall of 95.23%, and an F1-score of 95.10%, while KNN achieved an accuracy of 48.97%, precision of 68.59%, recall of 62.46%, and an F1-score of 65.32%. Based on these results, it can be concluded that Naïve Bayes is more effective and stable in classifying hoax and factual news based on TF-IDF representation.
Penerapan Algoritma Decision Tree dalam Analisis Sentimen Penerima MBG di Indonesia untuk Menilai Pandangan dan Preferensi Penerima Maulidia Safitri; Kustiyono
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.23-1.3510

Abstract

The Free Nutritious Meals Program (MBG) is one of the government’s efforts to improve the public’s nutritional status, making it important to analyze public opinion regarding this program on social media. This study aims to classify public sentiment toward the MBG Program using the Decision Tree algorithm. The research data was obtained from Instagram and X (Twitter), comprising 1,016 data points collected through scraping, preprocessing, and sentiment labeling. The research stages included text preprocessing, feature extraction using TF-IDF, and the application of k-fold cross-validation during model training and testing. The evaluation results show that the Decision Tree model achieved an accuracy of 89.07%, with a precision of 85.16% for the positive class and 95.93% for the negative class, as well as a recall of 97.35% for the positive class and 78.67% for the negative class. The classification results show that positive sentiment is slightly more dominant than negative sentiment on both social media platforms. These findings indicate that public opinion on social media tends to respond positively to the MBG Program, although the model still has limitations in recognizing negative sentiment in a balanced manner. This study also has limitations because the data comes from only two social media platforms, and the sentiment labeling process still has the potential to contain bias despite manual validation.
Co-Authors Abdul Aziz Abeda Muhammad Iqbal Ade Pratama Adilla Wira Wati Agung Wibowo Agung Wibowo Ahmad Ali Ajeng Ramadhanti Syafitri AL Thoriq Muhamad Hugo Alfan Afandi Alfiano Aldo Pamungkas Alfiano Aldo Pamungkas Ari Siswati Asrini Mahdia Daniel Manongga Darmin Darmin Dedi Muliadi Dina Lusiana Setyowati Eko Nur Hermansyah Ghufronul A, M Mufti gulo, titusman Hendri Saputra Hendrike Priventa Hevi Alvina Imam Taufik Irvan Pratama Putra Irwan Budiyono Izza Aulia Latif, Muhamad Lucyana Desy Anggraeni Maulidia Safitri Miswadi Miswadi Muhammad Fathoni, Muhammad Najib Noor Laila Ramadhani Pipit Sundari Priyanto Priyanto Putri Wulandari Ricky Darmawan Rosalina Wahyu Riani Sadam Rinto Sahlan M. Saleh Satria Avianda Nurcahyo Sigit Ambar Widyawati Sigit Kharisma Silvi Meilani Sofiyanti, Ida Solikah Teguh Harso Widagdo Ucta Pradema Sanjaya Umi Handayani Yeni Indraningtyas Yoga Zaen Vebrian Yoga Zaen Vebrian