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All Journal snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Prosiding SNATIF Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sistem Cerdas Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Bumigora Information Technology (BITe) ABIDUMASY : Jurnal Pengabdian kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) J-Intech (Journal of Information and Technology) Jurnal Sains dan Teknologi MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Knowbase : International Journal of Knowledge in Database Journal of Artificial Intelligence and Digital Business Bakti Sekawan : Jurnal Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Jurnal Pengabdian Sosial Journal of Management, Entrepreneurship and Tourism JUAN: Jurnal Pengabdian Nusantara Jurnal Pengabdian Pada Masyarakat IPTEKS Prosiding Seminar Nasional CORISINDO
Neny Sulistianingsih
Bumigora University
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INISIATIF PENGABDIAN MASYARAKAT UNTUK MENINGKATKAN KEMAMPUAN BAHASA INGGRIS MELALUI PELATIHAN TOEFL DARING Sutarman, Sutarman; Sulistianingsih, Neny; Sudewi, Ni Ketut Putri Nila
ABIDUMASY Vol 5 No 02 (2024): ABIDUMASY : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/abidumasy.v5i02.7356

Abstract

This community service initiative aimed to enhance English proficiency, specifically in the context of the TOEFL test, which is crucial for measuring language competence in today's globalized world. The webinar targeted teachers and students, providing a platform for interactive learning and discussion. The methodology included delivering theoretical knowledge along with practical exercises, focusing on listening, reading, and structure components of the TOEFL. The results showed high participant satisfaction, with an average score of 4.5 on the content quality and interaction. Participants expressed interest in diverse topics for future sessions, emphasizing the importance of continuous improvement in language training programs. The findings underscore the necessity of such community service programs to foster English language skills, thereby enhancing competitive capabilities in the global educational landscape.
Classification of Learning Styles of Junior High School Students Using Random Forest & XGBoost Algorithm Christine Eirene; Dian Syafitri; Neny Sulistianingsih; Khasnur Hidjah; Hairani Hairani
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i1.4913

Abstract

  Background: Accurately identifying students' learning styles so that educators can adjust their teaching methods accordingly is a challenge in the field of education. However, the application of Machine Learning for learning style classification has not yet been implemented in schools in Mataram City. Objective: This study aims to classify the learning styles of students at Junior high school (SMP) Negeri 2 Mataram using Random Forest and XGBoost algorithms.  Method: Data were collected through questionnaires completed by students in grades 7, 8, and 9. The results of data exploration (EDA) show data imbalance in the collected classes. Result: These results indicate that both algorithms performed well in classifying learning styles, with XGBoost showing slightly better performance. However, the accuracy obtained is not yet optimal, likely due to the limited dataset size. To address data imbalance, the SMOTE technique was applied. Initial evaluation showed that both XGBoost and Random Forest achieved an accuracy of 80%. After Hyperparameter Tuning, the accuracy of XGBoost increased to 84%, while Random Forest reached 82%. Conclusion: This study contributes to the application of Machine Learning in the education sector and highlights the need for further research to enhance model performance.  
Optimizing Water Hyacinth as Organic Fertilizer to Support Zero Waste and Green Economy Initiatives Martono, Galih Hendro; Neny Sulistianingsih; Ni Putu Sinta Dewi
ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 8 No. 2 (2025): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Muhammadiyah Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35568/abdimas.v8i2.6510

Abstract

The overgrowth of water hyacinth (Eichhornia crassipes) in Batujai Dam, located in West Lombok Regency, has become a serious environmental concern. Its uncontrolled spread has disrupted water flow, limited irrigation functions, and negatively impacted aquatic biodiversity. However, instead of treating it as a problem, this community service activity focused on turning the weed into a helpful resource—specifically, a raw material for producing organic fertilizer. Purpose: The aim of this community service was to raise public awareness and provide training on managing water hyacinth sustainably while creating added value for the local economy. The program was conducted with a small-scale fertilizer producer in Central Lombok. Method: Using a Participatory Action Research (PAR) approach, the activity involved the local community at every stage—from identifying the issue, designing solutions, and implementing the processing techniques to evaluating the results together. This approach was chosen to build community ownership and ensure the continuity of the efforts after the program ended. Result: As part of the process, around 1,000 kilograms of water hyacinth were harvested, sun-dried, chopped, and composted using Trichoderma spp. After fermentation, the community produced 20 liters of liquid fertilizer and 400 kilograms of solid compost. Conclusion: Beyond its environmental impact, the activity opened up opportunities for alternative income and promoted the concept of zero waste. It also encouraged the community to see local ecological issues not as obstacles but as opportunities to support green and sustainable living.
Machine Learning Approaches For Classification Of Infectious Diseases Using Smote Shofwan, Ari; Sulistianingsih, Neny; Rismayati, Ria
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v%vi%i.6960

Abstract

Infectious diseases such as acute nasopharyngitis, acute pharyngitis, and acute tonsillitis remain major public health issues, especially in primary healthcare facilities with limited resources like Puskesmas Gunungsari. This study aims to develop a machine learning-based classification model to detect infectious diseases using patient medical data. The evaluated models include Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network, with performance assessed using k-fold cross-validation ranging from 5 to 10 folds. Evaluation results show that the Decision Tree consistently achieved the best performance, with an accuracy of approximately 91.7% to 91.9% and an F1-score ranging from 91.9% to 92.3% on cross-validation data, as well as a test accuracy of 94.7% and an F1-score of 95.0%. The Random Forest model also demonstrated good and stable performance, with accuracy between 90.5% and 90.7%. Meanwhile, SVM and Neural Network produced lower results, with maximum accuracy of around 77.0% and 71.7%, respectively. Overall, the findings demonstrate that the Decision Tree model is the most effective for supporting early diagnosis of infectious diseases at Puskesmas Gunungsari, providing superior classification capabilities compared to other models.
Peningkatan Literasi Digital dan Bahasa Inggris melalui Pembuatan Konten Kreatif Sudewi, Ni Ketut Putri Nila; Dewi, Ni Putu Sinta; Satria, Christofer; Sulistianingsih, Neny; Syahid, Agus
Jurnal Pengabdian Sosial Vol. 2 No. 7 (2025): Mei
Publisher : PT. Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/p9kd0n89

Abstract

Perkembangan teknologi digital telah mengubah cara pembelajaran bahasa Inggris, mendorong perlunya integrasi literasi digital dalam proses pembelajaran. Kegiatan pengabdian ini bertujuan untuk meningkatkan keterampilan literasi digital dan komunikasi bahasa Inggris siswa SMA melalui pelatihan pembuatan konten kreatif. Kegiatan ini dilaksanakan di salah satu SMA di Kota Mataram dan melibatkan peserta didik kelas X dan XI. Metode pelaksanaan meliputi tahap observasi, pelatihan interaktif, praktik pembuatan konten digital dalam bahasa Inggris, serta evaluasi hasil. Hasil kegiatan menunjukkan peningkatan motivasi siswa dalam belajar bahasa Inggris serta peningkatan kemampuan mereka dalam mengakses, memahami, dan menghasilkan konten berbahasa Inggris secara digital. Kegiatan ini memberikan kontribusi nyata terhadap pengembangan kompetensi di era teknologi digital, khususnya keterampilan berpikir kritis, kreativitas, dan komunikasi. Oleh karena itu, pelatihan ini dapat dijadikan model dalam pembelajaran bahasa Inggris yang inovatif dan relevan dengan kebutuhan generasi digital.  
Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification Sulistianingsih, Neny; Martono, Galih Hendro
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1851

Abstract

The classification of edible versus poisonous mushrooms presents a critical challenge in the domains of applied biology and public health, particularly due to the serious implications of misidentification. This research employs the UCI Mushroom Dataset to evaluate and compare the effectiveness of several machine learning models, including traditional algorithms like Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes, as well as advanced ensemble techniques such as Stacking and Voting Classifier. Notably, both Random Forest and Stacking achieved flawless accuracy, reaching 100%, underscoring the high predictive capacity of these models in complex categorical scenarios. Conversely, Naïve Bayes exhibited significantly weaker performance—achieving only 59.8% accuracy—likely due to its underlying assumption of feature independence, which does not hold for this dataset. The ensemble learning approaches, including the combination of Stacking and Bagging, not only preserved but also enhanced model robustness and generalization. These methods effectively leverage the complementary strengths of individual learners to yield more accurate and stable predictions while mitigating overfitting risks. Comparative analysis with previous research confirms the consistency of these findings and reinforces the viability of ensemble strategies for handling intricate classification tasks. Overall, this study highlights the importance of algorithm selection tailored to data characteristics and supports the use of ensemble learning to boost predictive reliability.
The Use of Machine Learning in Social Media Sentiment Analysis: Communication Strategies in The Digital Age Noviansyah, Noviansyah; Krismono Triwijoyo, Bambang; Sulistianingsih, Neny
JMET: Journal of Management Entrepreneurship and Tourism Vol. 3 No. 2 (2025): July, Journal of Management Entrepreneurship and Tourism (JMET)
Publisher : Sumber Belajar Sejahtera

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61277/jmet.v3i2.216

Abstract

The development of digital technology has fundamentally transformed the way society communicates and consumes information, particularly through social media. Amidst the rapid and massive flow of information, sentiment analysis has become an essential tool for understanding public opinion. This study explores the use of machine learning as an analytical approach to identify and classify users' sentiments toward specific issues on social media. Through case studies on Twitter, Facebook, TikTok, and Instagram, machine learning algorithms such as Naive Bayes and Support Vector Machine were used to map public sentiment trends positive, negative, or neutral toward specific communication campaigns. The results indicate that machine learning can provide a faster, more accurate, and more dynamic sentiment analysis compared to manual methods. These findings serve as a strategic foundation for communication practitioners in designing more targeted, responsive, and data-driven messages. Thus, integrating machine learning into digital communication strategies not only enhances the effectiveness of message delivery but also strengthens the relationship between institutions and the public in an increasingly complex information age. 
Analisis Dampak Pelatihan Canva dalam Komunikasi Visual Sulistianingsih, Neny; Hasbullah, Hasbullah; Martono, Galih Hendro
Jurnal Pengabdian Pada Masyarakat IPTEKS Vol. 1 No. 2: Jurnal Pengabdian Pada Masyarakat IPTEKS, Juni 2024
Publisher : CV. Global Cendekia Inti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71094/jppmi.v1i2.52

Abstract

The use of Canva in educational communication has garnered attention, yet research exploring its use in announcements and communication with students remains limited. This study aims to optimize visual communication by providing Canva usage training to academic and program staff, with a focus on announcements and student communication. The engagement method follows a participatory approach and Service learning. Questionnaire results show a significant increase in confidence levels and graphic design abilities post-training. Positive social and behavioral changes are also observed. From a theoretical perspective, these findings are supported by visual design theories and service learning. Conclusions indicate that Canva training is effective in enhancing the quality of visual communication between educational institutions and students. Recommendations include continuing and expanding training and monitoring implementation outcomes.
Explainable Ensemble Learning for Maternal Health Risk in Low-Resource Settings Widyawati, Lilik; Sulistianingsih, Neny
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6765

Abstract

Maternal health remains a global challenge, particularly in low-resource settings where accurate and timely risk prediction is essential to reducing maternal mortality. This study proposes an explainable machine learning framework for predicting maternal health risks by integrating ensemble learning methods with SHAP (Shapley Additive exPlanations) for interpretability. This study utilized the publicly available Maternal Health Risk Data Set (MHRDS), comprising physiological features such as systolic and diastolic blood pressure, blood sugar level, body temperature, and age. A total of 18 machine learning models including Random Forest, XGBoost, LightGBM, Neural Networks, and TabNet were evaluated to compare individual classifiers and ensemble approaches comprehensively. The selection of this diverse set of models is grounded in the need to benchmark different algorithmic paradigms, as variations in inductive bias, learning capacity, and robustness to clinical data noise can influence predictive performance and generalizability. This comprehensive comparison enables the identification of optimal model types for integration into ensemble frameworks. Evaluation was performed across three different test scenarios (test sizes of 10%, 20%, and 30%) to assess model consistency under varying data partitions. Stacking, Voting, and Histogram-based Gradient Boosting showed consistently high performance, with Stacking achieving the highest accuracy of 87.2%, followed by Histogram Gradient Boosting (86.9%) and Voting (86.7%) at test size 0.2. SHAP analysis identified blood sugar, systolic blood pressure, and maternal age as the top predictors across all test scenarios. The best-performing models were deployed into a web-based clinical decision support system designed for healthcare practitioners in Indonesia. The proposed approach balances predictive accuracy and model transparency, offering a practical solution for improving maternal care in data-limited environments.
A Robust Gender Recognition System using Convolutional Neural Network on Indonesian Speaker Switrayana, I Nyoman; Hadi, Sirojul; Sulistianingsih, Neny
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3698

Abstract

Voice is one of the biometrics that humans have. Humans can be recognized by the sounds produced by their vocal cords and vocal tracts. One of the uses of voice is to recognize gender. Despite extensive research, gender recognition using machine learning remains unsatisfactory due to the complexity of voice features and the limitations of conventional algorithms. In this research, voice-based gender recognition is performed by applying deep learning. The deep learning model used is the Convolutional Neural Network (CNN). The input of CNN is the result of feature extraction from the Mel-Frequency Cepstral Coefficients (MFCC) method. MFCC produces Mel-Spectograms which are important features of sound. The dataset used is Indonesian speech. In the research, there are imbalanced and balanced dataset scenarios to see the performance of the model. To produce a balanced dataset, random undersampling is performed on the majority class. In addition, the effect of dividing training and testing data with a composition of 70:30, 80:20, and 90:10 was observed. The results show that the model has 100% accuracy for all imbalanced dataset scenarios. Then the highest accuracy is 99.65% for the balanced dataset scenario with 70:30 splitting. In summary, it can be concluded that CNN performs very well in identifying gender from voice features overall, although its performance decreases when random undersampling is applied to the dataset.
Co-Authors Abdul Muhid Advaita Hary, Adex Afrian, M.Alawi Agus Syahid Alfian Nurlifa Anthony Anggrawan Christine Eirene christofer satria Dewi, Ni Putu Sinta Dian Syafitri Dyah Susilowati Galih Hendro Martono Githa Alfiansyah Hairani Hairani Hammad, Rifqi Hasbullah Hasbullah Hasbullah Hasbullah Hidayat, Fadila Ananda Kartika I Gede Bayu Balawa Tangub I Nyoman Switrayana Irving Vitra Paputungan Kariyam - Khasnur Hidjah lauw, Christopher Michael Lilik Widyawati Lukmana, Aldi Mayasari, Novyta Indah Nafiani Nur Khamidah Ni Ketut Putri Nila Sudewi Ni Putu Sinta Dewi Noviansyah Noviansyah, Noviansyah Nurramadhan, Aliva Putradi, Angga Ria Rismayati Rismayanti, Ria Rismayati, Ria Setyawan, Ari Shofwan, Ari Sirojul Hadi Sri Kusumadewi Sutarman Sutarman Syahrir, Moch. Tanwir, Tanwir Tomi Tri Sujaka Triwijoyo, Bambang Krismono Uswatun Hasanah Vira Megasari Haqni