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All Journal snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Prosiding SNATIF Sistemasi: Jurnal Sistem Informasi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Bumigora Information Technology (BITe) ABIDUMASY : Jurnal Pengabdian kepada Masyarakat Jurnal Computer Science and Information Technology (CoSciTech) J-Intech (Journal of Information and Technology) Jurnal Minfo Polgan (JMP) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Knowbase : International Journal of Knowledge in Database Bakti Sekawan : Jurnal Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL Jurnal Pengabdian Sosial Journal of Management, Entrepreneurship and Tourism JUAN: Jurnal Pengabdian Nusantara Jurnal Pengabdian kepada Masyarakat IPTEKS Jurnal Pengabdian Pada Masyarakat IPTEKS
Neny Sulistianingsih
Bumigora University
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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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. 
Optimizing Autism Spectrum Disorder Identification with Dimensionality Reduction Technique and K-Medoid Martono, Galih Hendro; Sulistianingsih, Neny
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1142

Abstract

This research addresses the challenges of diagnosing and treating Autism Spectrum Disorder (ASD) using dimensionality reduction techniques and machine learning approaches. Challenges in social interaction, communication, and repetitive behaviours characterize ASD. The dimension reduction used in this research aims to identify what features influence autism cases. Several dimension data reduction techniques used in this research include PCA, Isomap, t-SNE, LLE, and factor analysis, using metrics such as Purity, silhouette score, and the Fowlkes-Mallows index. The machine learning approach applied in this study is k-medoid. By employing this method, our goal is to pinpoint the unique characteristics of autism that may facilitate the detection and diagnosis process. The data used in this research is a dataset collected for autism screening in adults. This dataset contains 20 features: ten behavioural features (AQ-10-Adult) and ten individual characteristics. The results indicate that Factor Analysis outperforms other methods based on purity metrics. However, due to data structure issues, the t-SNE method cannot be evaluated using purity metrics. PCA and LLE consistently provide stable silhouette scores across different values. The Fowlkes-Mallows index results closely align, but t-SNE tends to yield lower values. The choice of algorithm requires careful consideration of preferred metrics and data characteristics. Factor analysis is adequate for Purity, while PCA and LLE consistently perform well. This research aims to improve the accuracy of ASD identification, thereby enhancing diagnostic and treatment precision.
Perbandingan Algoritma Sarima dan Prophet Untuk Peramalan Trend Penjualan Voucher Game Online Rizki, M; Priyanto, Dadang; Martono, Galih Hendro; Sulistianingsih, Neny; Syahrir, Moch
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15083

Abstract

Industri game online terus mengalami perkembangan pesat, mendorong kebutuhan akan sistem peramalan yang akurat untuk mendukung pengambilan keputusan strategis dalam manajemen penjualan dan promosi. Studi ini bertujuan untuk membandingkan kinerja dua algoritma peramalan deret waktu, yaitu Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Prophet, dalam memprediksi tren penjualan voucher game online di platform Kiyystore. Data yang digunakan dalam penelitian ini mencakup transaksi historis dari tahun 2022 hingga 2024, dengan total 5,530 data penjualan. Studi ini menerapkan metodologi Cross Industry Standard Process for Data Mining (CRISP DM) yang terdiri dari tahap pemahaman bisnis, pemrosesan data, pemodelan, dan evaluasi. Model SARIMA dipilih karena kemampuannya untuk menangkap pola musiman dan tren dalam data stasioner. Sementara itu, Prophet digunakan karena dirancang untuk menangani tren non-linear, pola musiman, dan anomali secara otomatis. Evaluasi kinerja dari kedua algoritma dilakukan menggunakan dua metrik utama, yaitu Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan bahwa Prophet unggul dalam metrik MAE dengan nilai 0,7054, yang menunjukkan kinerja yang lebih baik dalam meminimalkan kesalahan rata-rata. Di sisi lain, SARIMA menunjukkan keunggulan dalam metrik RMSE dengan nilai 0,9514, yang berarti model ini lebih efektif dalam menangani kesalahan besar atau pencilan dalam prediksi. Studi ini memberikan kontribusi penting dalam pemilihan metode peramalan yang sesuai dengan karakteristik data. Dengan memahami keunggulan masing-masing algoritma, pelaku industri game online dapat lebih optimal dalam merencanakan strategi stok dan promosi, sehingga meningkatkan efisiensi dan daya saing bisnis secara keseluruhan
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.
Enhancing Predictive Models: An In-depth Analysis of Feature Selection Techniques Coupled with Boosting Algorithms Sulistianingsih, Neny; Martono, Galih Hendro
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3788

Abstract

This research addresses the critical need to enhance predictive models for fetal health classification using Cardiotocography (CTG) data. The literature review underscores challenges in imbalanced labels, feature selection, and efficient data handling. This paper aims to enhance predictive models for fetal health classification using Cardiotocography (CTG) data by addressing challenges related to imbalanced labels, feature selection, and efficient data handling. The study uses Recursive Feature Elimination (RFE) and boosting algorithms (XGBoost, AdaBoost, LightGBM, CATBoost, and Histogram-Based Boosting) to refine model performance. The results reveal notable variations in precision, Recall, F1-Score, accuracy, and AUC across different algorithms and RFE applications. Notably, Random Forest with XGBoost exhibits superior performance in precision (0.940), Recall (0.890), F1-Score (0.920), accuracy (0.950), and AUC (0.960). Conversely, Logistic Regression with AdaBoost demonstrates lower performance. The absence of RFE also impacts model effectiveness. In conclusion, the study successfully employs RFE and boosting algorithms to enhance fetal health classification models, contributing valuable insights for improved prenatal diagnosis.
Co-Authors Agus Syahid Alfian Nurlifa Christine Eirene christofer satria Dewi, Ni Putu Sinta Dian Syafitri Galih Hendro Martono Hairani Hairani Hasbullah Hasbullah Hasbullah Hasbullah I Nyoman Switrayana Irving Vitra Paputungan Kariyam - Khasnur Hidjah Krismono Triwijoyo, Bambang Muhid , Abdul Nafiani Nur Khamidah Ni Ketut Putri Nila Sudewi Ni Putu Sinta Dewi Noviansyah Noviansyah, Noviansyah Rismayati, Ria Rizki, M Shofwan, Ari Sirojul Hadi Sri Kusumadewi Sutarman Sutarman Syahrir, Moch. Uswatun Hasanah Vira Megasari Haqni