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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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Pengembangan Skill Masyarakat dalam Peningkatan Ekonomi secara Digital Sulistianingsih, Neny; Martono, Galih Hendro
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol 3 No 2 (2023): Desember
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v3i2.408

Abstract

Salah satu bentuk usaha dalam masyarakat yang banyak ditemui adalah perdagangan atau jual beli. Fenomena usaha jual beli mengalami perubahan yang sangat besar seiring dengan perkembangan teknologi informasi terutama perkembangan internet. Awalnya penerapan teknologi informasi dalam jual beli online dilakukan melalui promosi dengan webiste. Namun sejak munculnya media sosial Facebook di tahun 2006, promosi tidak hanya melalui internet saja namun juga media sosial. Perkembangan selanjutnya, promosi tidak hanya melalui media sosial namun juga melalui platform-platform yang secara khusus digunakan untuk jual beli secara online. Melihat hal ini perlu suatu upaya untuk peningkatan kemampuan masyarakat dalam penerapan teknologi informasi yang dapat membantu dalam jual beli secara online. Kegiatan pengabdian kepada masyarakat ini dilakukan untuk membantu masyarakat dalam memperkenalkan proses jual beli secara online atau promosi secara digital. Lebih lanjut, pelatihan dengan praktek secara langsung juga dilakukan. Hasil dari kegiatan pengabdian ini adalah menciptakan masyarakat yang melek terhadap teknologi informasi dan mampu berusaha secara mandiri untuk melakukan promosi serta jual beli secara online.
Visualisasi data twitter menjadi graph untuk social network analysis Galih Hendro Martono; Sulistianingsih, Neny
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.5722

Abstract

Twitter merupakan salah satu media sosial yang banyak digunakan di Indonesia. Sebagai salah satu media sosial, Twitter banyak digunakan untuk menyampaikan pendapat/opini, diskusi mengenai isu tertentu, untuk menyampaikan keluhan atau sentimen terhadap suatu produk, dan komunikasi politik. Data komunikasi pengguna Twitetr tersebut dapat diolah menjadi informasi yag bermanfaat untuk berbagai kepentingan sehingga perlu suatu cara untuk mengolah data Twitter. Perkembangan teknologi informasi memungkinkan untuk mengali informasi tersebut sehingga menjadi informasi yang berguna. Sebagai contoh, data Twitter dapat bermanfaat bagi perusahaan untuk melakukan profile konsumen sehingga dapat meningkatkan upaya pemasaran. Di bidang politik, data Twitter dapat digunakan untuk mencari orang yang memiliki pengaruh dalam Twitter yang dapat digunakan untuk membantu proses kampanye. Di bidang hukum, data Twitter tersebut dapat berguna untuk menganalisa jaringan serta distribusi informasi yang terkait dengan ujaran kebencian dan hoax. Untuk menganalisa data Twitter maka diperlukan suatu cara untuk mengubah data Twitter menjadi data graph sehingga dapat dianalisa lebih lanjut. Visualisasi data Twitetr menjadi data graph dilakukan karena terdapat perbedaan format data. Data Twitter berupa data string yang terdiri dari tweet yang merefleksikan komunikasi antar pengguna. Sedangkan data graph berupa kumpulan vertex dan edge yang dinotasikan sebagai . Vertex merepresentasikan pengguna Twitter dan edge merepresentasikan hubungan atau komunikasi antar pengguna. Penelitian ini bertujuan untuk membentuk data graph berdasarkan data Twitter sehingga dapat mempermudah analisa data Twitter untuk berbegai kepentingan.
PENINGKATAN KETERAMPILAN KOMUNIKASI BAHASA INGGRIS UNTUK SISWA SMA MELALUI WEBINAR INTERAKTIF Muhid , Abdul; Sudewi, Ni Ketut Putri Nila; Sulistianingsih, Neny
JUAN: Jurnal Pengabdian Nusantara Vol. 1 No. 3 (2024): Juli 2024
Publisher : CV Sentra Nusa Connection

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63545/juan.v1.i3.28

Abstract

Perkembangan teknologi informasi dan komunikasi telah mengubah dunia pendidikan menjadi lebih berpusat pada siswa. Keterampilan berbahasa Inggris sangat penting, terutama di sektor industri dan pendidikan di Indonesia. Namun, pembelajaran bahasa Inggris, khususnya keterampilan listening, masih menghadapi banyak tantangan. Penggunaan teknologi interaktif dan metode pembelajaran inovatif seperti aplikasi BBC Learning English, CD, video, dan permainan anagram telah menunjukkan hasil yang positif dalam meningkatkan keterampilan bahasa Inggris siswa. Kegiatan pengabdian ini bertujuan untuk mengatasi kesenjangan dalam keterampilan komunikasi bahasa Inggris di kalangan siswa SMA dan mahasiswa dengan metode partisipatif dan interaktif. Hasil evaluasi menunjukkan sebagian besar peserta puas dengan konten dan kualitas materi, meskipun durasi dianggap terlalu singkat. Evaluasi lebih lanjut diperlukan untuk meningkatkan kualitas pengabdian di masa mendatang.
Enhancing Stroke Diagnosis with Machine Learning and SHAP-Based Explainable AI Models Galih Hendro Martono; Neny Sulistianingsih
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8720

Abstract

Stroke is a serious illness that needs to be treated quickly to enhance patient outcome. Machine Learning (ML) offers promising potential for automated stroke detection through precise neuroimaging analysis. Although existing research has explored ML applications in stroke medicine, challenges remain, such as validation concerns and limitations within available datasets. The study aims to compare ML models and SHapley Additive exPlanations (SHAP) algorithm insights for stroke detection optimization. The research evaluates classifiers' performance, including Deep Neural Networks (DNN), AdaBoost, Support Vector Machines (SVM), and XGBoost, using data from www.kaggle.com. Results demonstrate XGBoost's superior performance across various data splits, emphasizing its effectiveness for stroke prediction. Utilizing SHAP provides deeper insights into stroke risk factors, facilitating comprehensive risk assessment. Overall, the study contributes to advancing stroke detection methodologies and highlights ML's role in enhancing clinical practice in stroke medicine. Further research could explore additional datasets and advanced ML algorithms to enhance prediction accuracy and preventive measures.
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.
Identification of top influence users in disseminating information on the 2024 Indonesian National Election Sulistianingsih, Neny; Martono, Galih Hendro
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 14 No. 1 (2024): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v14i1.25-32

Abstract

Social media has a vital role in general elections in Indonesia because social media is one of the platforms used by presidential candidates for campaigns to gain public support. General elections in Indonesia occur every five years. Many tweets talk about presidential candidates approaching the national election period. Not least, some buzzers deliberately use Twitter to carry out propaganda against a candidate or to bring down other presidential candidates with their opinions because information can spread widely and quickly on Twitter. Based on this, it is necessary to identify influential users in disseminating information related to the 2024 National Election, especially on Twitter. Various centrality methods were used in this study to identify influence users in sharing information about the 2024 National Election such us Degree Centrality, Closeness Centrality, Harmonic Centrality, Eigenvector Centrality, and Load Centrality. For the evaluation in this study, the results of each method were compared to one another to measure the similarity and correlation between the ranking lists of users who were influential in disseminating information about the 2024 National Election.
Enhancing Predictive Models: An In-depth Analysis of Feature Selection Techniques Coupled with Boosting Algorithms Neny Sulistianingsih; Galih Hendro Martono
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : LPPM 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.
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.
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