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All Journal ELSE (Elementary School Education Journal) : Jurnal Pendidikan dan Pembelajaran Sekolah Dasar JOURNAL OF APPLIED INFORMATICS AND COMPUTING JSiI (Jurnal Sistem Informasi) Journal of Information Systems and Informatics Neo Teknika: Jurnal Ilmiah Teknologi International Journal of Advances in Data and Information Systems MEANS (Media Informasi Analisa dan Sistem) Jurnal UNITEK Journal La Multiapp Jurnal Pengabdian Masyarakat (ABDIRA) Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Mandiri IT Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Jurnal Masyarakat Madani Indonesia Indonesian Journal of Community Empowerment (IJCE) "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Jurnal Informatika dan Kesehatan INOVTEK Polbeng - Seri Informatika Jurnal Ilmu Komputer
Sri Mujiyono
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Pelatihan Pengelolaan Website untuk Guru SD Negeri 5 Lerep Kabupaten Semarang Mujiyono, Sri; Sanjaya, Ucta Pradema; Wibisono, Iwan Setiawan; Rizqi, Hesti Yunitiara
Jurnal Masyarakat Madani Indonesia Vol. 4 No. 2 (2025): Mei
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/7n11gm95

Abstract

Transformasi digital dalam pendidikan menjadi kebutuhan mendesak di era akselerasi teknologi, namun keterbatasan literasi teknis di kalangan pendidik dan peserta didik menghambat optimalisasi infrastruktur yang tersedia. Studi kasus di SD Negeri 5 Lerep menunjukkan absennya platform digital resmi sebagai sarana penunjang pembelajaran, padahal website dapat menjadi ekosistem dinamis untuk kolaborasi dan interaksi tanpa batas geografis. Minimnya keterampilan teknis guru menyebabkan potensi transformatif teknologi terabaikan, sehingga diperlukan solusi strategis seperti pelatihan intensif pembuatan website. Program Pengabdian kepada Masyarakat (PkM) dirancang untuk mengubah pendidik dari konsumen pasif menjadi arsitek konten digital, membangun identitas digital sekolah, dan menciptakan ekosistem belajar yang adaptif dan personal. Kegiatan PkM ini berhasil mengembangkan website berbasis WordPress dengan pendekatan hybrid, menggabungkan metode waterfall dan agile, serta fitur seperti drag-and-drop builder dan plugin LearnDash. Hasilnya, terbentuk portal yang tidak hanya memfasilitasi administrasi terpusat, tetapi juga membuka kanal komunikasi transparan antara guru, orang tua, dan murid. Portal aktif SD Negeri 5 Lerep menjadi model replikabel untuk institusi pendidikan dengan keterbatasan serupa, menciptakan jejaring kolaboratif berkelanjutan yang mendorong revolusi cara berpikir dalam pendidikan.
Implementasi Algoritma XGBoost Untuk Prediksi Status Gizi Balita Berbasis Website Pangestu, Andi; Mujiyono, Sri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2390

Abstract

Malnutrition among toddlers remains a serious public health issue in Indonesia, with a stunting prevalence of 21.6% in 2022—still above the WHO standard, which sets the maximum threshold at 20%. Traditional methods for assessing nutritional status are time-consuming and prone to human error, highlighting the need for a more efficient and accurate approach. This study aims to develop a system for predicting toddler nutritional status using the XGBoost algorithm, implemented in a web-based application utilizing anthropometric data. A quantitative approach with applied research methods was used, analyzing 5,489 anthropometric records of children from RSUD DR. Gondo Suwarno during the 2017–2023 period, selected through purposive sampling. The dataset included parameters such as age, sex, height, weight, arm circumference, and head circumference of children aged 0–59 months. After data cleaning, 5,169 high-quality samples were retained and divided into 80% training and 20% testing sets with balanced class distribution. The XGBoost model was optimized using Grid Search with 3-fold cross-validation to achieve the best hyperparameter configuration. Results showed that the XGBoost model achieved an accuracy of 97.17%, precision of 97.16%, recall of 97.17%, and F1-score of 97.16% in classifying three nutritional status categories: Normal, Overnutrition, and Undernutrition. Feature importance analysis revealed that weight was the strongest predictor, contributing 42.52%, followed by age (16.79%) and height (15.49%). The system was successfully implemented in a user-friendly web application that allows the input of anthropometric data and provides real-time prediction results. This research produced an effective screening tool for early detection of toddler malnutrition, improving healthcare service efficiency and supporting government programs aimed at reducing stunting rates.
Aplikasi Rekomendasi Menu Makanan Harian Menggunakan Algoritma Metode KNN Nafi, Tri Maula; Mujiyono, Sri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2491

Abstract

This food menu recommendation app utilizes the K-Nearest Neighbors (KNN) algorithm to provide menu suggestions that suit the nutritional needs and preferences of users. The system analyzes various factors, including calories, protein, fat, and carbohydrates, in order to generate accurate and relevant recommendations. Users can enter information related to their nutritional needs and food preferences, such as their favorite types of food and allergies, to get the right menu suggestions. Through this application, users are expected to receive healthy menu recommendations that suit their individual needs, which in turn can increase awareness of the importance of a nutritious diet and overall health. With a data-driven approach, this app is an effective solution for those who want to make healthier food choices and support the achievement of their desired health and nutritional goals. In addition, this app can also contribute to building better eating habits among the community.
Sistem Pendukung Keputusan untuk Menentukan Guru Terbaik dengan Metode Analytical Hierarchy Process (AHP) : Studi kasus: SDN Bergas Lor 01 Khafid, Ahmad Noor; Mujiyono, Sri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2543

Abstract

Teacher performance evaluation is very important in ensuring the quality of education. However, this process is often difficult because it must consider various aspects, such as personality, competence, achievement, and innovation. In this study, we developed a decision support system (DSS) using the Analytical Hierarchy Process (AHP) method to help determine the best teachers objectively. This method helps identify relevant criteria and weights in determining teacher performance evaluations. This method is capable of producing more consistent and transparent decisions. The designed system is expected to simplify the decision-making process in determining the best teachers more efficiently, thereby supporting the improvement of teacher performance.
Pendampingan Penerapan E-Learning Interaktif Untuk Meningkatkan Literasi Digital Guru Dan Siswa Di SMA Negeri 1 Susukan Mujiyono, Sri; Rohman, Abdul; Pratama, Ade; Sanjaya, Ucta Pradema; Suryani, Ela
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 1 (2026): Februari
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/f6s7gr46

Abstract

Perkembangan teknologi dalam dunia pendidikan menuntut guru dan siswa memiliki kemampuan literasi digital yang kuat, khususnya dalam penggunaan e-learning sebagai sarana pembelajaran. Kegiatan pengabdian ini bertujuan memberikan pendampingan penggunaan e-learning interaktif bagi 15 guru dan 40 siswa di SMA Negeri 1 Susukan untuk meningkatkan keterampilan mereka dalam mengakses, mengolah, serta memanfaatkan teknologi pembelajaran digital. Pelaksanaan kegiatan dilakukan melalui tahap observasi awal, pelatihan penggunaan platform e-learning interaktif, pendampingan praktik, serta evaluasi melalui pre-test dan post-test. Hasil pelaksanaan menunjukkan peningkatan rata-rata skor sebesar 34,4 poin pada guru (dari 48,2 menjadi 82,6) dan 31,4 poin pada siswa (dari 48,7 menjadi 80,1) pada skala 0-100. Terjadi peningkatan yang signifikan dalam pemahaman guru dan siswa terhadap berbagai fitur e-learning, seperti pembuatan kelas virtual, penyampaian materi interaktif, pemberian tugas, dan evaluasi berbasis online. Selain itu, peserta mengalami peningkatan literasi digital terkait kemampuan mengakses informasi, mengelola data, serta menggunakan teknologi untuk mendukung proses belajar mengajar, dengan 90% guru dan 85% siswa menyatakan peningkatan kepercayaan diri dan kemandirian belajar. Secara keseluruhan, program pendampingan ini terbukti efektif dalam meningkatkan kompetensi digital guru dan siswa serta memperkuat penerapan pembelajaran berbasis teknologi di sekolah.
Sistem Pendukung Keputusan dalam Pemilihan Smartphone dengan Menerapkan Metode Simple Additive Weighting (SAW) Amalia, Alifah; Mujiyono, Sri
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 1 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i1.4722

Abstract

Perkembangan industri smartphone yang sangat pesat menghadirkan beragam merek, tipe, dan fitur dengan spesifikasi yang semakin kompleks. Kondisi ini sering kali menyulitkan konsumen dalam menentukan pilihan smartphone yang sesuai dengan kebutuhan dan kemampuan, sehingga keputusan pembelian tidak jarang didasarkan pada faktor subjektif seperti gengsi atau tren semata. Oleh karena itu, diperlukan suatu sistem pendukung keputusan (SPK) yang mampu membantu konsumen dalam mengambil keputusan secara objektif dan rasional. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pendukung keputusan dalam pemilihan smartphone dengan menerapkan metode Simple Additive Weighting (SAW). Metode SAW dipilih karena mampu melakukan proses penilaian dan perankingan alternatif berdasarkan sejumlah kriteria dan bobot yang telah ditentukan. Penelitian ini menggunakan metode studi literatur dan observasi langsung di beberapa toko handphone untuk memperoleh data kriteria, alternatif, dan bobot penilaian. Tahapan metode SAW meliputi pemberian nilai alternatif, normalisasi matriks keputusan, pembobotan, dan perhitungan nilai preferensi untuk menentukan peringkat smartphone terbaik. Hasil penelitian menunjukkan bahwa sistem pendukung keputusan yang dibangun mampu memberikan rekomendasi smartphone secara cepat, objektif, dan sesuai dengan kebutuhan konsumen. Dengan demikian, penerapan metode SAW dalam sistem pendukung keputusan ini dapat meningkatkan efektivitas proses pemilihan smartphone serta membantu konsumen dan karyawan toko dalam memberikan keputusan yang lebih akurat dan efisien.
Penerapan Algoritma Naive Bayes dengan optimasi genetic algorithm untuk memprediksi kedisiplinan siswa Dewanto, Bernadus; Mujiyono, Sri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3084

Abstract

The grouping of student misconduct data for the second semester is used to assess student discipline levels. This data classification uses data mining methods to determine student discipline objectively. The data mining method used in this study is Naïve Bayes. This data classification uses manual calculations with the Gaussian Naïve Bayes method, which uses an integer approach. It is not only tested manually but also with RapidMiner tools. The technique used in Rapid Miner to divide the data into several parts or folds, where the training and testing data parts are divided by cross-validation. This technique aims to make the evaluation results more accurate. The evaluation is made with a confusion matrix with curation, precision, and recall calculations and F1 score. Data grouping is divided into two categories, namely disciplined and undisciplined. The results of the study using Naïve Bayes with GA optimization obtained an accuracy value of 89.47% using the cross-validation technique with stratified sampling type, which helped produce a more stable evaluation.
Analysis of Public Sentiment Towards the Free Nutritious Meals Program on TikTok Social Media Using the K-Nearest Neighbor Algorithm Nugroho, Ivan; Mujiyono, Sri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11849

Abstract

The Free Nutritious Meals Program is currently one of the most talked about public policies, generating a wide range of responses from the public. One of the most active discussion forums is the social media platform TikTok, given that it has a large number of users and a relaxed and informal style of language. This study aims to examine public sentiment toward the MBG program through TikTok user comments, while also testing the performance of the K-Nearest Neighbor (KNN) algorithm in classifying sentiment as positive or negative. Research data was collected by crawling comments on several TikTok videos discussing Free Nutritious Meals during the period from September to November 2025. A total of 1,000 comments were obtained and then processed through data cleaning stages, such as data cleaning, case folding, normalization, tokenization, stopword removal, and stemming. To convert the text into numerical form, the Term Frequency–Inverse Document Frequency (TF-IDF) method was used. Meanwhile, sentiment labeling was done manually to maintain the quality of the training data. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score indicators. The test results showed that the best accuracy rate, which was 70.50%, was obtained at a K value of 4. From the sentiment analysis conducted, negative comments were found to outnumber positive sentiments. The criticism that emerged generally related to food quality and safety, inequality in program distribution, and a lack of transparency in information provided to the public. This study shows that the KNN algorithm is quite capable of being used for sentiment analysis on TikTok comment data, although it still has limitations in understanding the variety of informal language often used by users. Therefore, the results of this study are expected to provide public opinion-based input for policymakers, as well as a foundation for the development of sentiment analysis methods that are more suited to the characteristics of social media in future studies.
DECISION TREE BASED INTERNET SIGNAL QUALITY ANALYSIS ON TELKOM INFRASTRUCTURE Yeni Fitri Afidah; Sri Mujiyono
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 2 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v6i2.4949

Abstract

This study analyzes internet signal quality on Telkom infrastructure using the Decision Tree algorithm. Utilizing 5,000 data points from Kaggle, the research classifies network quality into three categories: Good, Fair, and Poor based on parameters such as download speed, ping latency, and packet loss. The evaluation results show that the Decision Tree model achieved an accuracy rate of 98%. Parameters such as download speed and ping latency were identified as the most dominant factors in determining signal quality. These findings prove that a machine learning approach is effective in generating easily interpretable decision rules for network service optimization.
Segmentasi Fuzzy C-Means Untuk Membantu Identifikasi Kualitas Beras Berdasarkan Nilai Threshold, Warna Dan Ukuran Iwan Setiawan Wibisono; Sri Mujiyono
Multimatrix Vol. 1 No. 1 (2018)
Publisher : Universitas Ngudi Waluyo

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

Abstract

Abstrak— There are several types of rice circulating in Indonesian society, namely: fragrant pandan rice, rojolele, membramo, IR 64, IR 42, C4, etc. To get rice quality assurance, it is necessary to check the quality of rice which is usually done by experienced inspectors. This study aims to produce a tool for inspectors who can process the image of rice and classify the quality of rice and analyze the performance of the classification system. The steps that will be carried out include: preprocessing, feature extraction, and classification. The feature extraction method used is Statistical Feature Extraction in terms of its texture which is one of the physical characteristics of rice. While for classifying quality using the Fuzzy C-Means (FC-M) method. From the results of the study, it was found that the 3 final cluster centers were center cluster 1 (5.89333; 2.05), center cluster 2 (6.28199; 2.546), and center cluster 3 (6.96583; 2.999167) and validation was generated amounting to 92.82%.Keywords— Klasifikasi Beras. Image Processing, FC-M, Computer Vison
Co-Authors Abdul Rohman Abdul Rohman Ade Pratama Ade Pratama Afif Faisal Yasin Amalia, Alifah Anton Anton Aripin, Ahmad Nurul Dewanto, Bernadus Ela Suryani Fahrizal Alfian Dante fifi maqfiroh Fiktina Vifri Ismiriyam Galih Agil Febri Hidayatullah Hani Latifah Heni Setyowati Hesti Yunitiara Rizqi Ilham, Dimas Muhammad Iwan Setiawan Wibisono Kartika Yuni Purwanti Khafid, Ahmad Noor Lisa Virdinarti Putra Manao, Mikel Frewinta Nafi, Tri Maula Novan Syaiful Nugroho, Aventura Suryo Nugroho, Ivan Pangestu, Andi Polinus Gulo Puput Amanda Rasyid Purnamasari, Yessy Sabilla Sanjaya, Ucta Pradema Suamanda Ika Novichasari Syarifudin, Nanang Irfan Wahyu Kristiningrum Widayati Widayati Yeni Fitri Afidah Zulmi Roestika Rini