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Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Published by Universitas Pahlawan Tuanku Tambusai
ISSN : 29619890     EISSN : 29619890     DOI : https://doi.org/10.31004/jerkin.v1i1
Core Subject : Humanities, Education,
Humanities Education Languange, Linguistic, Communication & Media Mathematics Other
Jurnal Pengabdian Masyarakat dan Riset Pendidikan is a journal on Faculty of Education. Jurnal JERKIN: Jurnal Pengabdian Masyarakat dan Riset Pendidikan is under the auspices of the Faculty of Education, Universitas Pahlawan Tuanku Tambusai. The journal is registered with E-ISSN: 2961-9890. Jurnal Pengabdian Masyarakat dan Riset Pendidikan is published four times a year in September, December, March and June. Jurnal Pengabdian Masyarakat dan Riset Pendidikan receives 50 articles per issue. The journal publishes articles in mathematics education including teaching and learning, instruction, curriculum development, learning environments, teacher education, educational technology, educational developments, from many kinds of research such as survey, research and development, experimental research, classroom action research, etc.
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 3,563 Documents
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Analisa Komparasi Kinerja Algoritma K-Nearest Neighbor (K-NN) dan Decision Tree dalam Klasifikasi Situs Web Phising: Penelitian Prasetyo, Fajar Dwi; Maulana, Muhammad; Ramadhan, Faris; Setiabudi, Ananda Lutfi; Budiawan, Imam; Desmulyati, Desmulyati
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4965

Abstract

Phishing attacks represent a significant cybersecurity threat aimed at stealing sensitive user information through psychological manipulation using fake websites. Conventional detection methods relying on blacklists are considered ineffective in recognizing zero-day attacks or newly published phishing sites. This study aims to develop an automated detection model using a Machine Learning approach by comparing the performance of two Supervised Learning algorithms: K-Nearest Neighbor (K-NN) and Decision Tree. The dataset used is sourced from the UCI Machine Learning Repository, consisting of 11,055 records with 30 URL characteristic features. Performance evaluation was conducted using Accuracy metrics and Confusion Matrix analysis. Experimental results indicate that the Decision Tree algorithm significantly outperforms K-NN with an accuracy of 95.21%, while K-NN achieved an accuracy of only 60.11%. Furthermore, Decision Tree demonstrated a very low False Negative rate, making it a more recommended model for real-time cybersecurity system implementation.
Pengaruh Metode Project-Based Learning terhadap Kemampuan Berbicara Bahasa Inggris Siswa: Penelitian Dewi Sartipa
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4966

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh metode Project-Based Learning (PBL) terhadap kemampuan berbicara bahasa Inggris siswa melalui pendekatan studi literatur. Kemampuan berbicara merupakan keterampilan penting dalam pembelajaran bahasa Inggris, namun sering kali menjadi aspek yang paling menantang bagi siswa karena membutuhkan penguasaan kosakata, struktur bahasa, dan keberanian untuk mengekspresikan gagasan secara lisan. Metode PBL menekankan keterlibatan aktif siswa dalam proyek yang bermakna, autentik, dan relevan dengan kehidupan nyata, sehingga siswa terdorong untuk berkomunikasi, berkolaborasi, dan menyampaikan ide secara lisan. Penelitian ini menggunakan tahapan sistematis, mulai dari penentuan fokus kajian, penelusuran literatur melalui basis data daring dan jurnal bereputasi, seleksi literatur berdasarkan relevansi dan kualitas, analisis deskriptif-kualitatif, hingga sintesis temuan lintas penelitian. Hasil analisis menunjukkan bahwa PBL secara konsisten meningkatkan kemampuan berbicara siswa dari berbagai jenjang pendidikan. PBL berkontribusi pada peningkatan partisipasi aktif, kepercayaan diri, kelancaran berbicara, keterampilan monolog, dan kemampuan komunikasi kontekstual. Integrasi teknologi, seperti augmented reality dan kecerdasan buatan, semakin memperkuat efektivitas PBL dalam menciptakan pembelajaran yang interaktif dan bermakna. Selain itu, PBL memungkinkan penerapan bahasa Inggris dalam konteks kebutuhan nyata, seperti promosi kesehatan, ekowisata, dan perhotelan. Temuan ini menunjukkan bahwa PBL merupakan pendekatan yang efektif, holistik, dan relevan untuk pengembangan kemampuan berbicara bahasa Inggris. Penelitian ini memberikan dasar konseptual dan rekomendasi praktis bagi pendidik dalam merancang pembelajaran berbicara yang lebih aktif, kolaboratif, dan autentik
Analisis Pengelompokan Pola Pembayaran UKT Mahasiswa Menggunakan Algoritma K-Means Clustering: Penelitian Desmulyati, Desmulyati; Budiawan, Imam; Andrianto, Feri; Canavaro, Reafael Andrian; Nugroho, Muhammad Haikal; Saputra, Sofiyan Aris
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4967

Abstract

Single Tuition Fee (UKT) plays a crucial role in financing higher education, but late and arrear payments are often difficult to analyze manually. This study aims to classify student UKT payment patterns using the K-Means algorithm based on per capita income, UKT amount, lateness, lateness category, and total arrears. The data used were 300 cleaned and standardized students. The number of clusters was determined using the Elbow and Silhouette Score methods, with the best results at k = 3 (SSE = 524.06; Silhouette Score = 0.5609). The three clusters include high-income students with regular payments, low-income students with minor delays, and high-risk students with large delays and arrears. These results help universities map UKT payment risks and develop more targeted collection and relief policies.
Analisa Prediksi Mahasiswa Penerima KIP-K menggunakan Algoritma Naive Bayes: Penelitian Desmulyati, Desmulyati; Mulyono, Muhammad Jadetz; Maulana, Amriandry; Raihan, Muhammad Ibnu; Sumitra, Ridwan Sholeh; Mukhtar, Ali
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4969

Abstract

The Indonesia Pintar–Kuliah card (KIP-K) program is a government-funded educational assistance initiative aimed at supporting financially disadvantaged students. The selection process requires accurate data analysis to ensure that the assistance is distributed appropriately. This study aims to develop a classification model for predicting KIP-K recipients using the Naive Bayes algorithm based on several attributes, including family income, number of dependents, housing condition, parents’ occupation, social assistance status, GPA, attendance, and income per capita. A dataset of 200 student records was preprocessed and encoded before the model was trained using an 80:20 train–test split. The model’s performance was evaluated through accuracy, precision, recall, and F1-score metrics. The results indicate that the Naive Bayes algorithm achieves satisfactory classification performance, with an accuracy score of (insert your model accuracy). These findings highlight the potential of machine learning techniques to support a more objective and efficient selection process for KIP-K recipients.
Analisis Kepuasan Pelanggan terhadap Beberapa Produk yang di Jual di E-Commerce Menggunakan Metode Naïve Bayes dan Logistic Regression: Penelitian Alam, Java Diovanka; Ramdhan, Musyaffa; Rafael, Muhammad Yuzakki Raja; Hamka, Muhammad Faiz; Desmulyati, Desmulyati; Budiawan, Imam
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4970

Abstract

Customer satisfaction is a crucial element that plays a significant role in the sustainability of businesses in the e-commerce sector. Reviews provided by consumers serve as an important source of information to assess how satisfied they are with the products they purchased. This study aims to evaluate customer satisfaction levels using product review data through two classification methods: Multinomial Naive Bayes and Logistic Regression. The data used comes from a real Indonesian-language dataset that includes review texts and buyer ratings. The research process consists of several stages, starting from text preprocessing, feature extraction using the TF-IDF method, satisfaction label grouping, model training, and evaluation using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The findings of this study indicate that both methods can predict customer satisfaction with competitive accuracy. Logistic Regression demonstrates more consistent results compared to Naive Bayes in the context of Indonesian-language text. These results can be utilized by e-commerce companies to monitor product quality and continuously improve services for consumers.
Panduan Identifikasi Plankton di Laboratorium: Penelitian Alianto, Alianto; Vera Sabariah; Tresia Sonya Tururaja; Sadida Anindya Bahtiar
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4971

Abstract

After knowing how to take and handle plankton water samples and knowing and understanding how to use a microscope, the next problem that arises is how to count or observe and identify plankton water samples with a microscope in the laboratory. Based on this, this community service aims to describe in detail how to identify plankton in the laboratory. The tools and materials used for plankton identification in the laboratory consist of a reversible microscope, preparations, Sedwig rafter cells, coverglass, dropper pipettes, counters, plankton water sample bottles, spray bottles, notebooks or HVS paper, 2B pencils, erasers and tissues. The stages of plankton identification in the laboratory consist of the preparation stage and the implementation stage. The preparation stage consists of preparations outside the laboratory and in the laboratory. Preparations outside the laboratory include plankton water sample bottles, spray bottles, counters, notebooks or HVS paper, 2B pencils, erasers and tissues. The most important preparation outside the laboratory is to prepare plankton identification sheets and plankton identification books. Preparations in the laboratory include a reciprocating microscope, preparations or SRC, dropper pipettes, and coverglass. The implementation stage of plankton identification in the laboratory consists of 18 procedures that must be followed sequentially. Plankton identification includes 18 procedures with a primary focus on sample loading, microscope setup, determination methods, and plankton counts.
Perbandingan Model Machine Learning dalam Prediksi Penyakit Jantung dengan Optimalisasi Fitur Gejala dan Faktor Risiko: Penelitian Wardhana, Ade Ikhsanudin Setiawan; Fadlil, Galih Min; Wirahman, Raihan Putra; Fahrani, Deny Wahyu; Budiawan, Imam; Desmulyati, Desmulyati
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4972

Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early detection of its risk crucial to prevent severe complications. This study develops a heart disease risk prediction system using machine learning techniques, including Random Forest, Logistic Regression, and Support Vector Machine (SVM). The dataset is processed through several stages, including numerical feature selection, feature engineering with the addition of a total symptoms variable, and class imbalance handling using class-weight adjustments The model training process involves splitting the data into training and testing sets, followed by evaluation using accuracy, confusion matrix, and classification report metrics. The system also integrates an interactive interface that allows users to select symptoms and risk factors through widget-based checklists, enabling real-time prediction. The results show that the best-performing model achieves high accuracy and effectively identifies the most influential factors based on feature importance analysis. These findings indicate that machine learning provides a reliable and efficient tool to support early risk detection of heart disease.
Pengabdian Terhadap Masyarakat: Pelatihan Coding dalam Pendekatan Deep Learning : Penelitian Zulfadewina, Zulfadewina; Ithriyah, Siti; Meilana, Septi Fitri; Ninawati, Mimin
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4973

Abstract

The development of digital technology requires elementary school teachers to have strong digital literacy, including the ability to integrate coding into learning. However, a needs analysis showed that teachers at SDN Batu Ampar 02 still struggled to understand basic coding concepts and were unable to apply them in their learning as required by the Independent Curriculum. Teachers' Scratch and Deep Learning skills will be improved by this Community Partnership Program (PKM). Lessons, demonstrations, hands-on practice, and mentoring were used. Teachers' comprehension improved significantly, marked by the ability to create simple coding projects and develop meaningful deep learning-based learning activities. This program successfully built teachers' readiness to integrate digital technology as a 21st-century learning medium
Pencegahan Bullying dan Penguatan Kesehatan Mental Melalui Edukasi dan Konseling Fitrah: Pengabdian Malim Soleh Rambe; Vitria Larseman Dela; Khairul Amri; Sukatno; Azijah Tussolihah Siregar; Eli Marlina Harahap; Ermaliza
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4974

Abstract

This community service activity aims to prevent bullying and strengthen the mental health of elementary school students through holistic education and natural counseling. The background is the increasing psychosocial vulnerability of students due to social dynamics and limited mentoring services in schools. The method used is a descriptive participatory approach at Muhammadiyah Hutalambung Elementary School, South Tapanuli Regency, involving students in grades IV–VI and teachers. Activities include problem identification, anti-bullying education based on natural values, group natural counseling, integration of character values ​​in learning and extracurricular activities, and reflective evaluation. Data were collected through observation, reflection, and documentation, and analyzed qualitatively. The results show an increase in students' understanding of bullying, empathy, emotional regulation, and self-confidence. In addition, teachers experienced increased capacity in preventive and responsive mentoring, resulting in an inclusive, safe school climate oriented towards strengthening students' character and psychological well-being in a sustainable manner.
Analisis Prediksi Nilai Akhir Mahasiswa Menggunakan Algoritma Regresi Linear Berbasis Machine Learning pada Program Studi Teknologi Informasi Universitas Bina Sarana Informatika: Penelitian Salsabila, Khalisa; Maulidia, Nahya Faulya; Hafid, Shabrina Auliya Zahra; Balqis, Aisyah Shinta; Budiawan, Imam; Desmulyati, Desmulyati
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4975

Abstract

The development of information technology in education demands a fast, objective, and data-driven academic evaluation system. Problems in higher education often involve lecturers' difficulty in monitoring and predicting student academic performance early, resulting in delayed response to declining performance. One solution that can be implemented is the use of Machine Learning. This study aims to analyze the prediction of students' final grades using a Machine Learning-based Linear Regression algorithm with attendance and assignment grades as variables. The case study was conducted on students of the Information Technology Study Program at Bina Sarana Informatika University using simulated data of 100 students, with the data divided into 80% training and 20% testing. Model evaluation used MSE, RMSE, and R². The results showed an R² value of 0.94, which means that 94% of the variation in students' final grades can be explained by attendance and assignment grades, while 6% is influenced by other factors. These findings indicate that the Linear Regression algorithm has excellent predictive performance in predicting students' final grades objectively and data-driven.

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