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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 Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Teknologi Informasi dan Multimedia 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) Jurnal Sains dan Teknologi MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Journal Of Information System And Artificial Intelligence 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 kepada Masyarakat IPTEKS Jurnal Pengabdian Pada Masyarakat IPTEKS Prosiding Seminar Nasional CORISINDO
Neny Sulistianingsih
Bumigora University
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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.
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.
Analisis Sentimen Dampak Putusan MK Batas Usia Minimum Capres-Cawapres dengan SVM, Naïve Bayes, dan KNN Lukmana, Aldi; Martono, Galih Hendro; Sulistianingsih, Neny
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5523

Abstract

Mahkamah Konstitusi (MK) berperan penting dalam menegakkan konstitusi, termasuk menetapkan batas usia minimum pencalonan Presiden dan Wakil Presiden. Putusan ini memicu beragam reaksi di media sosial, mulai dari dukungan hingga penolakan yang dinilai politis. Penelitian ini bertujuan menganalisis sentimen publik terhadap putusan tersebut menggunakan Support Vector Machine (SVM), Naïve Bayes (NB), dan K-Nearest Neighbors (KNN), serta membandingkan kinerjanya berdasarkan akurasi, presisi, recall, dan F1-score. Penelitian dilakukan melalui enam tahap: (1) Business Understanding – menentukan kebutuhan, tujuan, dan pengumpulan data; (2) Data Understanding – mengumpulkan, mendeskripsikan, dan mengevaluasi kualitas data; (3) Data Preparation – membersihkan, memilih, dan mentransformasi data; (4) Modelling – menerapkan algoritma SVM, NB, dan KNN; (5) Evaluation – mengukur kinerja model menggunakan confusion matrix; serta (6) Deployment – menyusun laporan dan dokumentasi hasil analisis. Data diambil dari media sosial X dan YouTube, diolah menggunakan teknik text mining dan machine learning. Hasil menunjukkan SVM dan KNN memiliki akurasi tertinggi, masing-masing 89,5%, sedangkan NB mencapai 88,5%, sehingga SVM dan KNN dinilai lebih efektif dalam menganalisis sentimen publik terhadap putusan MK.
Pengembangan Sistem Pakar Menggunakan Metode Rule-Based Reasoning untuk Memprediksi Waktu Pelaksanaan Kegiatan Pertanian dan Melaut Berdasarkan Sistem Penanggalan Wariga Putradi, Angga; Hidjah, Khasnur; Sulistianingsih, Neny
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5529

Abstract

Wariga merupakan sistem penanggalan tradisional masyarakat Suku Sasak yang digunakan untuk menentukan hari baik dalam berbagai aspek kehidupan seperti bertani, melaut, membangun rumah, dan melaksanakan ritual adat. Pengetahuan ini bersifat lokal dan umumnya hanya dikuasai oleh Maestro Wariga. Namun, keberadaan Maestro semakin langka, sehingga dibutuhkan inovasi digital untuk melestarikan dan mendokumentasikan pengetahuan tersebut. Penelitian ini bertujuan untuk membangun sistem pakar berbasis aturan (Rule-Based System) guna merepresentasikan logika penentuan hari baik secara digital, sebagaimana dilakukan Maestro Wariga. Sistem dirancang menggunakan metode IF–AND–AND–THEN berdasarkan kombinasi tanggal adat, naptu hari, dan zona wilayah. Fokus penelitian berada di wilayah Bayan, Kabupaten Lombok Utara. Pengujian dilakukan melalui perbandingan hasil sistem dengan perhitungan manual Maestro dan uji Black-Box untuk memastikan fungsionalitas sistem. Hasilnya menunjukkan bahwa sistem memiliki tingkat kesesuaian tinggi dan berjalan sesuai logika yang dirancang. Diharapkan, sistem ini menjadi media aksesibel bagi generasi muda untuk memahami Wariga sekaligus sebagai upaya pelestarian budaya lokal melalui pemanfaatan teknologi. 
Perbandingan Algoritma Machine Learning Untuk Mendeteksi Gagal Jantung Berbasis Seleksi Fitur Rfecv Dan Penyeimbangan Data Smote Setyawan, Ari; Sulistianingsih, Neny; Rismayati, Ria
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5605

Abstract

Deteksi dini gagal jantung merupakan tantangan signifikan dalam dunia medis karena kompleksitas faktor risikonya. Penelitian ini bertujuan membandingkan kinerja enam algoritma machine learning dalam memprediksi risiko gagal jantung dengan pendekatan CRISP-DM. Data klinis sebanyak 299 pasien diproses melalui seleksi fitur menggunakan Recursive Feature Elimination with Cross-validation (RFECV) serta penyeimbangan kelas dengan Synthetic Minority Over-sampling Technique (SMOTE). Algoritma yang dievaluasi meliputi Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, dan Gradient Boosting. Evaluasi dilakukan menggunakan validasi silang berstrata dengan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan Random Forest mencapai performa terbaik dengan akurasi dan F1-score sebesar 91,20%, diikuti Gradient Boosting dengan 90,20%. Implementasi SMOTE terbukti meningkatkan kemampuan model, terutama dalam mendeteksi kelas minoritas. Temuan ini menegaskan bahwa metode ensemble seperti Random Forest, dikombinasikan dengan RFECV dan SMOTE, efektif untuk klasifikasi risiko gagal jantung secara akurat dan andal.
Prediction of Patient Arrivals per Room at NTB Provincial Hospital Using the Auto SARIMA Model Mayasari, Novyta Indah; Sulistianingsih, Neny; Rismayanti, Ria
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5629

Abstract

Rumah Sakit Umum Daerah (RSUD) Provinsi NTB merupakan rumah sakit rujukan utama di Nusa Tenggara Barat yang menghadapi tantangan dalam mengelola fluktuasi jumlah pasien. Lonjakan pasien dapat menyebabkan kekurangan sumber daya medis, sementara penurunan jumlah pasien berisiko menyebabkan pemborosan sumber daya. Oleh karena itu, diperlukan sistem prediksi yang mampu memperkirakan jumlah kedatangan pasien secara akurat guna mendukung manajemen rumah sakit. Penelitian ini bertujuan untuk mengembangkan model prediksi jumlah pasien menggunakan Auto SARIMA (Seasonal AutoRegressive Integrated Moving Average). Data yang digunakan mencakup informasi tanggal masuk, tanggal keluar, asal masuk (IGD atau poli), serta jenis pembayaran pasien (BPJS, PBI, NPBI, dan umum). Selain itu, model ini mempertimbangkan faktor eksternal seperti hari libur, kondisi cuaca, dan kejadian khusus yang berpotensi memengaruhi jumlah pasien.Tahapan penelitian meliputi pengumpulan dan pra-pemrosesan data, pemodelan menggunakan Auto SARIMA, serta evaluasi hasil prediksi dengan Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE). Hasil evaluasi model dengan menggunakan metrik statistik menunjukkan performa yang cukup baik, dengan nilai Mean Absolute Error (MAE) sebesar 3,61 dan Root Mean Square Error (RMSE) sebesar 5,33. Nilai ini mengindikasikan bahwa tingkat kesalahan prediksi relatif kecil, sehingga model Auto SARIMA dapat diandalkan dalam memperkirakan jumlah pasien per kamar. Prediksi Auto SARIMA memberikan manfaat praktis bagi rumah sakit. Pola musiman kedatangan pasien yang teridentifikasi dapat digunakan untuk mengoptimalkan pengelolaan kamar rawat inap, perencanaan sumber daya medis, serta penjadwalan pelayanan kesehatan secara lebih efisien.
Feature importance of using explanaible artificial intelligence (xai) and machine learning for diabetes disease classification Ahmad, Muhammad Maulana; Sulistianingsih, Neny; Hidjah, Khasnur
Journal Of Information System And Artificial Intelligence Vol. 6 No. 1 (2025): Vol. 6 No.1(2025): Journal of Information System and Artificial Intelligence Vo
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v6i1.252

Abstract

Diabetes is one of the most significant global health problems in the modern era. This disease not only has a serious impact on the quality of life of sufferers, but also poses a great economic and social burden, both for individuals and the health service system as a whole. Therefore, early detection and effective treatment are very important in an effort to reduce the prevalence and negative impact of this disease. Therefore, the purpose of this study is to design a machine learning classification model that is able to identify feature importance with the help of the Explainable Artificial Intelligence (XAI) method in the case of diabetes. This model is expected to provide a clear interpretation of the most relevant features or symptoms, making it easier to detect whether a person has diabetes or not based on the symptoms that have been selected more optimally. The results of this study in the treatment or prediction of diabetes show that the results of the selection of LIME model features are higher than the accuracy of the SHAP model, where the highest is the LIME model which is processed using classification using the XGBoost algorithm with an accuracy of 98.47%, in addition to the LIME model using the Decisien Tree and Random Forest algorithms producing an accuracy of 91.97% and 91.49%, respectively. then the SHAP model using the XGBoost algorithm produced an accuracy of 0.9094%, the Decisien Tree algorithm produced an accuracy of 0.8059% and the Random Forest produced an accuracy of 88.46%, with the amount of data used as many as 70000 data, with 80% training data and 20% test data. The findings of this study are that the LIME feature selection combined with the XGBoost classification method has the best accuracy rate of 98.47% compared to the SHAP feature selection which is the same in combination with XGBoost with an accuracy of 90.94%. These findings also show that the selection of LIME features combined with the XGBoost algorithm is able to improve the interpretability of the model as well as maintain or even improve the accuracy of the predictions. This approach allows for the identification of the most relevant features more efficiently, thus supporting more informed decision-making in the data analysis process
Co-Authors Abdul Muhid Afrian, M.Alawi Agus Syahid Ahmad, Muhammad Maulana Alfian Nurlifa Christine Eirene christofer satria Dewi, Ni Putu Sinta Dian Syafitri Galih Hendro Martono Hairani Hairani Hammad, Rifqi Hasbullah Hasbullah Hasbullah Hasbullah Hidayat, Fadila Ananda Kartika I Nyoman Switrayana Irving Vitra Paputungan Kariyam - Khasnur Hidjah 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 Putri, Angelica Davina Meisya Ria Rismayati Rismayanti, Ria Rismayati, Ria Rizki, M Setyawan, Ari Shofwan, Ari Sirojul Hadi Sri Kusumadewi Sutarman Sutarman Syahrir, Moch. Triwijoyo, Bambang Krismono Uswatun Hasanah Vira Megasari Haqni