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All Journal Jurnal Edukasi Universitas Jember E-Dimas: Jurnal Pengabdian kepada Masyarakat JIKO (Jurnal Informatika dan Komputer) Jurnal Dimensi Pendidikan dan Pembelajaran Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Prosiding Seminar Nasional Teknoka Jurnal Informatika Universitas Pamulang JURNAL SILOGISME : Kajian Ilmu Matematika dan Pembelajarannya Adimas : Jurnal Pengabdian Kepada Masyarakat KOMPUTEK EXPLORER International Journal Artificial Intelligent and Informatics SinarFe7 Jurnal Borneo Informatika dan Teknik Komputer (JBIT) JEECS (Journal of Electrical Engineering and Computer Sciences) Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen Sains Data Jurnal Studi Matematika dan Teknologi Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Dyah Mustikasari
Universitas Muhammadiyah Ponorogo
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Other

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Penggunaan Algoritma K-means untuk Menentukan Calon Penerima Beasiswa Intan Putri Permatasari; Fajaryanto Cobantoro, Adi; Mustikasari, Dyah
SinarFe7 Vol. 7 No. 1 (2025): SinarFe7-7 2025
Publisher : FORTEI Regional VII Jawa Timur

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Abstract

pemberian beasiswa merupakan salah satu bentuk dukungan finansial yang diberikan kepada mahasiswa yang membutuhkan agar dapat mengakses pendidikan tinggi secara berkualitas. Namun, dalam proses seleksi penerima beasiswa, seringkali terjadi kendala seperti subjektivitas penilaian, lamanya proses seleksi, dan kurang tepatnya sasaran penerima beasiswa. Penelitian ini bertujuan untuk mengimplementasikan algoritma K-Means Clustering dalam menentukan calon penerima beasiswa secara lebih objektif, efisien, dan akurat. Metode K-Means digunakan untuk mengelompokkan mahasiswa berdasarkan kriteria seperti IPK, penghasilan orang tua, dan jumlah tanggungan keluarga. Data mahasiswa digunakan sebagai input dalam proses clustering, yang kemudian dikelompokkan ke dalam tiga kategori, yaitu layak, dipertimbangkan, dan tidak layak menerima beasiswa. Metode K-Means digunakan untuk mengelompokkan data mahasiswa berdasarkan kedekatan nilai-nilai tersebut terhadap centroid awal. Hasil penelitian menunjukkan bahwa sistem dapat mengelompokkan 461 mahasiswa ke dalam tiga cluster dengan akurasi sebesar 86.8%. Data yang digunakan adalah data mahasiswa dari Program Studi Teknik Informatika Universitas Muhammadiyah Ponorogo. Selain itu, sistem ini diharapkan juga mampu memberikan kemudahan dalam proses pengelolaan data dan pengambilan keputusan untuk pemberian beasiswa secara lebih objektif dan efisien. Dengan adanya sistem ini, diharapkan proses seleksi calon penerima beasiswa dapat dilakukan secara lebih sistematis dan transparan. Dari penelitian ini diharapkan menunjukkan bahwa algoritma K-Means mampu memberikan pengelompokan yang lebih sistematis dan membantu panitia dalam pengambilan keputusan pemberian beasiswa secara lebih cepat dan transparan. Dengan demikian, implementasi algoritma K-Means dapat menjadi solusi efektif dalam meningkatkan akurasi dan efisiensi proses seleksi penerima beasiswa
Sistem Pendukung Keputusan Untuk Deteksi Dini Pada Balita Stunting Menggunakan Metode Fuzzy Tsukamoto ARDILO IQBAL BRILYAN; Mustikasari, Dyah; Sugianti, Sugianti
SinarFe7 Vol. 7 No. 1 (2025): SinarFe7-7 2025
Publisher : FORTEI Regional VII Jawa Timur

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Abstract

Abstrak - Stunting merupakan masalah kesehatan serius yang mempengaruhi pertumbuhan dan perkembangan balita di Indonesia, sehingga diperlukan deteksi dini untuk mencegah dampak jangka panjangnya. Penelitian ini bertujuan untuk mengimplementasikan sistem deteksi dini stunting pada balita menggunakan metode Fuzzy Inference System (FIS) dengan pendekatan Tsukamoto. Permasalahan yang diangkat adalah bagaimana merancang sistem pendukung keputusan yang dapat menganalisis usia, berat badan, dan tinggi badan balita secara efisien untuk menghasilkan tingkat risiko stunting. Penelitian dilakukan dengan metode waterfall mulai dari identifikasi masalah, pengumpulan data melalui studi literatur dan wawancara, desain sistem, hingga implementasi dan pengujian menggunakan black box. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan output diagnosis yang akurat dan sesuai dengan perhitungan manual, serta seluruh fitur sistem berjalan baik, sehingga sistem ini dapat menjadi alat bantu yang efektif dalam mendeteksi risiko stunting secara dini dan berbasis data.
Pelatihan Pengemasan Tahu dan Sosialisasi Website Tahumurni.id Dukuh Taji Desa Gelanglor, Kecamatan Sukorejo, Ponorogo Dyah Mustikasari; Jamilah Karaman; Riza Dessy Nila Ayutika
KREATIF: Jurnal Pengabdian Masyarakat Nusantara Vol. 5 No. 3 (2025): Jurnal Pengabdian Masyarakat Nusantara
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/kreatif.v5i3.8421

Abstract

This community service program was designed to empower tofu micro, small, and medium enterprises (MSMEs) located in Dukuh Taji, Gelanglor Village, Sukorejo District, Ponorogo. The main focus of the program was to improve the quality and competitiveness of tofu products through training on modern packaging techniques and the introduction of digital marketing platforms. The implementation method combined training sessions and mentoring activities that provided participants with both theoretical knowledge and practical skills. In particular, the introduction of vacuum sealer technology allowed tofu MSMEs to produce packaging that is more hygienic, durable, and visually appealing, which significantly increased the added value of their products. In addition, participants were introduced to the use of the website tahumurni.id as a platform for online promotion and sales. Through guided practice, tofu producers successfully learned how to upload product information, manage online transactions, and engage with customers digitally. The outcomes of this program demonstrated that tofu MSMEs not only improved their product packaging but also expanded their marketing reach by entering online markets. This program made a significant contribution to strengthening the competitiveness of local tofu enterprises by integrating technological innovation with effective digital marketing strategies.
Implementation of the Forward Chaining Algorithm in a Student Mental Health Detection System Ridwan Yulindra Megananda; Ghulam Asrofi Buntoro; Dyah Mustikasari
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 2 (2026): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i2.2

Abstract

Student mental health is an important aspect in supporting academic success and individual well-being. The various academic pressures, social challenges, and the transition to independent living make students a vulnerable group to mental health disorders such as stress, anxiety, and depression. However, many students are still reluctant or experience difficulties in accessing professional services to solve this problem. This research aims to develop an early detection system for student mental health based on an expert system using Forward Chaining. This method performs reasoning from symptoms toward a conclusion based on mental health condition using rules stored in the knowledge base. The system is developed using the DASS-21 (Depression, Anxiety, Stress Scale-21) instrument to assist in identifying mental health conditions. The dataset consists of 50 student respondents from Universitas Muhammadiyah Ponorogo who completed the DASS-21 questionnaire. System performance was evaluated by comparing the diagnostic outputs of the system against the standard DASS-21 score. The results were analyzed using a confusion matrix to calculate accuracy, precision, recall, and F1-score per severity class. The research results show that the system is capable of initially identifying students’ mental health conditions by presenting the severity level of the mental condition, a description of the condition, and appropriate handling recommendations. Black-box testing confirmed the accuracy of 96%, with precision and recall values above 90% across all severity classes. These results demonstrate that the implemented forward chaining system provides an accessible, automated, and standardized tool for early mental health detection in the Indonesian higher education context.
Implementasi Metode Regresi Linear Berganda untuk Prediksi Harga Penjualan Material Paving Block pada CV. Difa Jaya Abadi Andyra Kurniawan; Dyah Mustikasari; Andy Triyanto Pujo Raharjo
Sains Data Jurnal Studi Matematika dan Teknologi Vol 3, No 2: July-December 2025
Publisher : Institut Nurul Islam Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52620/sainsdata.v3i2.281

Abstract

Peningkatan kebutuhan material konstruksi menjadikan paving block sebagai salah satu produk dengan permintaan tinggi di pasaran. CV. Difa Jaya Abadi sebagai produsen paving block memerlukan sistem prediksi harga yang akurat untuk mendukung strategi penjualan dan efisiensi produksi. Penelitian ini mengimplementasikan metode Regresi Linear Berganda untuk memprediksi harga jual paving block per meter persegi berdasarkan variabel produksi, biaya produksi, upah pekerja, bulan, dan tahun. Sistem prediksi dikembangkan berbasis web menggunakan Python (Flask) untuk backend perhitungan, HTML/CSS untuk antarmuka, dan PostgreSQL sebagai basis data. Data historis periode 2021–2024 digunakan sebagai dasar pelatihan model, sedangkan evaluasi dilakukan menggunakan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan prediksi harga yang mendekati nilai aktual dengan tingkat akurasi yang baik, di mana nilai MAPE sebesar 0,6%. Implementasi sistem ini diharapkan dapat membantu perusahaan dalam menetapkan harga jual yang lebih tepat, meningkatkan efisiensi operasional, dan mendukung pengambilan keputusan berbasis data.
Using SVM and KNN for Predicting Customer Response Sentiment of M-PAJAK Application Muhammad Titan Rama Adi Wijaya; Ida Widaningrum; Angga Prasetyo; Dyah Mustikasari
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.4528

Abstract

M-Pajak, an application initiated by the Directorate General of Taxes, signifies the modernization of taxation and serves a crucial function. This application facilitates taxpayers in meeting their tax obligations. User satisfaction with this application may be assessed via reviews on the Google Play Store. While this application fulfills client satisfaction, its sustained success is significantly contingent upon user contentment and experience. Sentiment analysis is essential for elucidating user evaluations and interactions with the program. This research analyses the sentiment of M-Pajak application reviews on Google Play using Support Vector Machine (SVM) and K-Nearest Neighbour (KNN), supported by the Term Frequency-inverse Document Frequency (TF-IDF) feature extraction method. A total of 1000 reviews between December 11, 2022 and December 2, 2023 were processed using KNN and SVM. The KNN algorithm yielded 153 positive predictions and 847 negative predictions and achieved 94% of accuracy. Meanwhile, SVM achieved an accuracy of 88.10%, with 325 positive predictions and 675 negative predictions. The results demonstrate the superiority of KNN in sentiment classification of M-Pajak reviews. This study also indicates that negative comments outnumber positive ones in this application. This serves as a signal for the Directorate General of Taxation to enhance user satisfaction with the M-Pajak application through continuous updates.
Comparative Performance Analysis of SVM, LSTM, and IndoBERT for Sentiment Analysis of User Reviews Muhammad Arsayuan Wijaya; Indah Puji Astuti; Dyah Mustikasari
Infoman's : Jurnal Ilmu-ilmu Informatika dan Manajemen Vol. 20 No. 1 (2026): Infoman's
Publisher : LPPM & Fakultas Teknologi Informasi UNSAP

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Abstract

The Maganghub Kemnaker National Internship Program, launched on October 1, 2025, attracted significant public attention while also generating both criticism and praise on the social media platform X. This study compares the performance of three classification algorithms—Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and IndoBERT—for sentiment analysis of public opinions regarding the program. The dataset consists of 6,825 Indonesian-language tweets collected between October and December 2025 after duplicate removal. The data were preprocessed and labeled as positive, neutral, or negative using two approaches: automated labeling with a Hugging Face model and manual annotation. Each algorithm was evaluated under five hyperparameter tuning scenarios using an 80:20 train–test split and assessed with accuracy, precision, recall, and F1-score. The results show that IndoBERT achieved the highest performance, with an accuracy of 85.27% using the Hugging Face–labeled dataset, outperforming SVM (79.05%) and LSTM (78.68%). IndoBERT's superior performance is attributed to its Transformer-based architecture and multi-head self-attention mechanism, which effectively capture bidirectional contextual information, including sarcastic expressions commonly found in social media posts. The findings were further implemented in a web-based dashboard to support interactive public opinion monitoring for policymakers.
Co-Authors Akzha Nabella Putra Arganata Andy Triyanto Pujo Raharjo Andyra Kurniawan ARDILO IQBAL BRILYAN Arifin, Rizal Arifin, Rizal Aslan Alwi Astuti, Arin Yuli Bellani Patnandi Budiono, Tri Sakti Desriyanti . Ellisia Kumalasari Erika Diyah Cahyani Erika, E Fajar Kurniawan Fajaryanto Cobantoro, Adi Ghulam Asrofi Buntoro Gita Lely Endika Putri Ida Widaningrum, Ida Ida Yeni Rahmawati Ika Nurjanah Indah Puji Astuti Intan Putri Permatasari Jamilah Karaman Karaman, Jamilah Muhammad Arsayuan Wijaya Muhammad Titan Rama Adi Wijaya Permatasari, Dian Wahyu Prasetyo, Angga Pratiwi, Nadia Intan Rafiq, Fuad Ainur Reyhan Putra Primanda Ridwan Yulindra Megananda Risky Khoirul Muklis Riza Dessy Nila Ayutika Siti Asiyah Sucipto Sucipto Sugianti, Sugianti