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All Journal International Journal Of Science, Technology & Management (IJSTM) Jurnal Pengabdian Masyarakat Gemilang (JPMG)
Nirmala Sari Hasibuan, Mila
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SOSIALISASI PERAN GURU DALAM MENANAMKAN KARAKTER DISIPLIN DAN TANGGUNG JAWAB TERHADAP PESERTA DIDIK DI SMA NEGERI 1 PANAI HULU KECAMATAN PANAI HULU KABUPATEN LABUHANBATU Nirmala Sari Hasibuan, Mila; Sari Hrp, Nurlina; Irmayanti; Rohayani Hsb, Elysa
Jurnal Pengabdian Masyarakat Gemilang (JPMG) Vol. 1 No. 1: Januari 2021
Publisher : HIMPUNAN DOSEN GEMILANG INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1019.64 KB) | DOI: 10.58369/jpmg.v1i1.11

Abstract

Kegiatan pengabdian masyarakat merupakan salah satu wujud dari tri darma perguruan tinggi dan merupakan salah satu tugas yang penting dilaksanakan oleh perguruan tinggi kepada masyarakat, dalam hal ini pelaksaan pengabdian masyarakat dilaksanakan di sekolan untuk kepentingan ilmu pengetahuan, teknologi dan seni beserta aplikasi dan pengembangannya sesuai dengan kemajuan zaman. Oleh karena itu perguruan tinggi memiliki tanggungjawab untuk turut serta aktif dalam berperan dan berpartisipasi untuk membentuk kualitas pendidikan. Kegiatan pengabdian masyarakat ini di dasarkan pada kesadaran akan arti pentingnya Peran Guru Dalam Menanamkan Karakter disiplin dan tanggung jawab kepada peserta didik dengan adanya kegiatan sosialisasi ini di harapkan para guru yang ada di SMA Negeri 1 Panai Hulu lebih paham bagaimana menanamkan disiplin dan rasa tanggung jawab kepada peserta didik, dan dapat mengaplikasikan hasil kegiatan ini di lingkungan sekolah, sehingga akan melahirkan yang disiplin bertanggung jawab, cerdas, berilmu serta berbudi pekerti luhur.
SOSIALISASI PERAN ORANG TUA DALAM MENINGKATKAN MINAT BACA ANAK DI DESA TELUK RAMPAH KECAMATAN TORGAMBA KABUPATEN LABUHANBATU SELATAN Nirmala Sari Hasibuan, Mila; Irmayanti; Dini Hariyati Adam; Indah Fitria Rahma; Siti Zahara Saragih
Jurnal Pengabdian Masyarakat Gemilang (JPMG) Vol. 1 No. 4: Juli 2021
Publisher : HIMPUNAN DOSEN GEMILANG INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (437.86 KB) | DOI: 10.58369/jpmg.v1i4.46

Abstract

Sosialisasi mengenai peran orangtua dalam meningkatkan minat baca anak di Desa Teluk Rampah Kecamatan Torgamba Kabupaten Labuhanbatu Selatan telah dilakukan pada tanggal 25 Februari 2021 di kantor kepala Desa Teluk Rampah. Kegiatan ini bertujuan uuntuk meningkatkan pemahaman orang tua yang mengikuti sosialisasi ini terhadap pentingnya peran orang tua dalam menumbuh kembangkan minat baca pada anak .Dalam kegiatan pengabdian ini yang menjadi khalayak sasaran adalah masyarakat Desa Teluk Rampah Kecamatan Torgamba Kabupaten Labuhanbatu Selatan yang hadir di balai desa yang berjumlah 20 orang, sosialisasi dilakukan dengan tetap memperhatikan dan mengacu pada protokuler kesehatan, mencuci tangan serta memakai masker. Sosialisasi ini menggunakan metode penyampain informasi untuk materi bagaimana cara menumbuh kembangkan minat baca pada anak serta tanya jawab yaitu membahas masalah sesuai judul sosialisasi. Secara keseluruhan sosialisasi memberikan manfaat bagi masyarakat tentang pentingnya peranan orang tua dalam menumbuh kembangkan minat baca pada anak, sehingga akan terbentuk generasi bangsa yang berwawasan dan berpandangan luas kedepan berkarakter yang kuat dan berbudi pekerti luhur. Secara kuantitas peserta sudah terpenuhi dan secara kualitatif peserta sangat puas karena sosialisasi ini memiliki perbedaan dengan sosialisasi lain sejenisnya.
Design Of A Budget Processing Information System At Sat Reskrim Of Polres Labuhanbatu Web Based Martasya Berkat Silaen, Dian; Juni Yanris, Gomal; Nirmala Sari Hasibuan, Mila; Rohayani Hasibuan, Elysa
International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i5.1163

Abstract

Capital expenditures carried out by regional governments produce infrastructure in an area both for providing basic services to the community and to encourage regional governments to provide potential sources of local revenue. The Financial Report of the National Police of the Republic of Indonesia consists of a budget realization report, balance sheet, operational report, report on changes in equity and notes on the financial report as attached, which is the responsibility of the National Police which has been prepared based on an adequate internal control system. The aim of this research is to be able to design an information system for processing budget data to unit of Sat Reskrim the Polres Labuhanbatu is web-based so that it can help operational work, especially in processing financial data, to be more effective and efficient
Comparison Of Support Vector Machine And Naïve Bayes Algorithms For Analyzing Public Interest In Espresso Coffee Rita, Sano; Halmi Dar, Muhammad; Nirmala Sari Hasibuan, Mila
International Journal of Science, Technology & Management Vol. 5 No. 4 (2024): July 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i1.814

Abstract

Given its increasing popularity, public interest in buying espresso coffee is an important concern for coffee industry players. To understand and predict this buying interest, the use of classification algorithms in data analysis is crucial. This study was conducted to compare the performance of two popular classification algorithms, namely Support Vector Machine and Naïve Bayes, in analyzing public interest in buying espresso coffee. This research problem is based on the need for an accurate predictive model in the coffee industry to aid in strategic decision-making related to marketing and sales. The proposed solution is to implement two different classification algorithms and assess their performance using a variety of performance evaluation metrics. The purpose of this study is to determine which algorithm is superior in terms of accuracy, precision, recall, and f1-score. The research method entails collecting data on public interest in purchasing espresso coffee, preprocessing data, implementing both algorithms, and evaluating each algorithm's performance. The results show that Naïve Bayes consistently outperforms Support Vector Machine in all performance evaluation metrics. Naïve Bayes achieved 94.00% accuracy, 91.40% precision, 100% recall, and 95.51% F1-Score, compared to Support Vector Machine, which achieved 90.00% accuracy, 88.60% precision, 96.90% recall, and 92.56% F1-Score. The conclusion of this study is that the Naïve Bayes classifier is more effective and efficient in predicting people's purchasing interest in espresso coffee compared to support vector machines. This advantage can be attributed to the ability of Naïve Bayes to handle data that may have non-normal distributions or independent variables.
Analysis of Public Satisfaction Levels Towards Hospital Services Using The K-Nearest Neighbors Method (Case Study: XYZ Regional Public Hospital) Ramadani Sibutar-Butar, Novia; Halmi Dar, Muhammad; Nirmala Sari Hasibuan, Mila
International Journal of Science, Technology & Management Vol. 6 No. 2 (2025): March 2025
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i2.1284

Abstract

The level of public satisfaction with health services is an important indicator that reflects the quality of services provided by a hospital. XYZ Hospital, as one of the main health service providers, strives to continuously improve the quality of its services by understanding and evaluating the level of patient satisfaction. However, challenges arise when it comes to accurately identifying and predicting patient satisfaction, given the diverse characteristics of patients and the complexity of the services provided. Therefore, this study aims to analyze the level of public satisfaction with XYZ Hospital services using the K-Nearest Neighbors method. This study employs a quantitative approach by utilizing patient satisfaction data obtained through a survey. We then analyze the data using the K-Nearest Neighbors method, known for its effectiveness in classifying based on data proximity. We carry out the model performance evaluation process through an evaluation matrix that includes accuracy, precision, recall, and F1-score. The results of the study show that the K-Nearest Neighbors model is able to classify patient satisfaction with an accuracy value of 94%, precision of 97.67%, recall of 95.45%, and F1-Score of 96.55%. These results indicate that the K-Nearest Neighbors model is not only accurate in predicting patient satisfaction but also consistent in classifying patients who are satisfied and dissatisfied. The study's conclusion is that the K-nearest neighbors method is very effective in analyzing and predicting the level of patient satisfaction at XYZ Hospital. This study makes a significant contribution by utilizing the K-Nearest Neighbors model as a potent predictive tool for assessing patient satisfaction, a tool hospitals can employ to enhance service quality. We hope that further development will enable the larger-scale implementation of this model, thereby enhancing the quality of health services across various hospitals.
Implementation of Deep Learning Models in Conducting Aspect-Based Sentiment Analysis Yusni Adhelina, Chintika; Halmi Dar, Muhammad; Nirmala Sari Hasibuan, Mila
International Journal of Science, Technology & Management Vol. 6 No. 4 (2025): July 2025
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i4.1251

Abstract

The increasing volume of consumer reviews on e-commerce platforms has highlighted the need for sentiment analysis methods capable of capturing user opinions more specifically concerning particular aspects of products or services. Aspect-Based Sentiment Analysis (ABSA) addresses this need by identifying the aspects discussed in a review and determining the polarity of sentiment expressed toward each aspect. This study aims to explore and compare the effectiveness of two deep learning models, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in performing ABSA on Indonesian-language e-commerce user reviews. The research methodology comprises several stages: data exploration and cleaning, text preprocessing, aspect and sentiment annotation, training of CNN and LSTM models, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. The dataset is divided into training, validation, and testing subsets. The analyzed aspects include delivery, product, price, application, and service. Results show that the LSTM model outperforms CNN across all evaluation metrics. LSTM achieved an accuracy of 86.10%, precision of 85.70%, recall of 85.90%, and an F1-score of 85.80%, while CNN reported slightly lower values. Based on these findings, LSTM proves to be more effective in understanding the contextual and linguistic structure of the Indonesian language in ABSA tasks. This study provides a valuable contribution to the development of automatic sentiment analysis systems in the e-commerce sector. Future research can expand this approach by incorporating transformer-based models such as IndoBERT or integrating attention mechanisms to further improve predictive accuracy. These findings offer practical insights for industry stakeholders seeking to enhance customer experience through a deeper understanding of user sentiment.
Performance Evaluation of Machine Learning Algorithms in Aspect-Based Sentiment Analysis on E-Commerce User Reviews Maharani Pakpahan, Mira; Halmi Dar, Muhammad; Nirmala Sari Hasibuan, Mila
International Journal of Science, Technology & Management Vol. 6 No. 4 (2025): July 2025
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i4.1255

Abstract

The rapid growth of the e-commerce industry in Indonesia has resulted in a significant surge in the number of user reviews available on various digital platforms. These reviews contain valuable information about customer experiences related to price, product quality, service, delivery, and applications. However, the massive volume of data and its unstructured nature pose challenges in extracting relevant information. Aspect-Based Sentiment Analysis (ABSA) presents an approach that can provide deeper insights by identifying sentiment towards specific aspects within a review, rather than just the overall general sentiment. This study aims to evaluate the performance of several machine learning algorithms, namely Naïve Bayes, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN), in implementing ABSA on e-commerce user reviews in Indonesia. The dataset used consists of 20,000 user reviews of the Shopee and Tokopedia applications obtained through a crawling process on the Google Play Store. The data is processed through several stages: text preprocessing, aspect and sentiment annotation, model training, and performance evaluation using accuracy, precision, recall, and F1-Score metrics. The evaluation results showed differences in performance among the tested algorithms. Naïve Bayes achieved an accuracy of 82.5%, KNN achieved 84.6%, Random Forest 87.1%, while SVM provided the best performance with an accuracy of 89.3% and an F1-Score of 88.3%. This difference in performance indicates that algorithms that are better able to handle high-dimensional text representations, such as SVM, are superior in aspect-based sentiment classification compared to other methods. Thus, this study not only provides a comprehensive overview of the effectiveness of machine learning algorithms in sentiment analysis in the e-commerce sector but also provides a practical basis for developing recommendation systems, improving customer service, and enhancing user experience strategies on digital platforms. This research is expected to serve as a reference in the application of machine learning to support the growth of the e-commerce industry in Indonesia.
Co-Authors Dini Hariyati Adam Halmi Dar, Muhammad Indah Fitria Rahma Irmayanti Juni Yanris, Gomal Maharani Pakpahan, Mira Martasya Berkat Silaen, Dian Ramadani Sibutar-Butar, Novia Rita, Sano Rohayani Hasibuan, Elysa Rohayani Hsb, Elysa Sari Hrp, Nurlina Siti Zahara Saragih Yusni Adhelina, Chintika