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All Journal International Journal of Advances in Intelligent Informatics MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Varian Jurnal Computer Science and Information Technology (CoSciTech) J-Intech (Journal of Information and Technology) 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 Pada Masyarakat IPTEKS Prosiding Seminar Nasional CORISINDO
Galih Hendro Martono
Universitas Bumigora, Mataram, Indonesia
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 Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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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.
Pelatihan Penggunaan Kuesioner Usability untuk Pengukuran Kualitas Perangkat Lunak bagi Mahasiswa dan Masyarakat Umum: Training on the Use of Usability Questionnaire for Measuring Software Quality for Students and the General Public Wahyuningrum, Tenia; Martono, Galih Hendro; Wardhana, Helna
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 8 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i8.9598

Abstract

Agile development ensures quality software is on time and according to user expectations. Therefore, software testing is essential before launch, including usability testing as part of the user experience. However, the many choices of questionnaires in usability testing make it difficult for developers to determine the appropriate method. This community service is a training activity that aims to improve the understanding of students and the general public, especially those involved in software development related to usability testing. The training is conducted face-to-face with counseling, namely interactive lectures, discussions, case studies, and quizzes. The training materials include an introduction to software quality and usability evaluation, respondent selection techniques, an introduction to types of usability questionnaires, and exercises in calculating and interpreting usability scores using the Usability Metric for User Experience (UMUX-Lite) questionnaire. The training results showed that the level of understanding was still low (31.07%); this result was due to several obstacles, namely the imbalance in the number of participants and facilitators, the relatively short discussion time, and not all participants being active in the activities. The strategy for future improvements is to conduct remedial sessions, re-evaluate learning methods and media, and divide participants into small groups with adequate training facilitators.
Penerapan ARAS dan TOPSIS pada Sistem Pendukung Keputusan Untuk Seleksi Penerimaan Anggota PPK di KPU Sumbawa Barat Ramadhan, Rahmat Adi Mulya; Husain, Husain; Martono, Galih Hendro
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.3015

Abstract

Dalam rangka meningkatkan transparansi dan akuntabilitas pada proses seleksi Panitia Pemilihan Kecamatan (PPK), Komisi Pemilihan Umum (KPU) Kabupaten Sumbawa Barat membutuhkan sistem seleksi yang objektif, terukur, dan dapat dipertanggungjawabkan. Selama ini seleksi masih dilakukan secara konvensional sehingga berpotensi menimbulkan subjektivitas dan kurang efisien. Penelitian ini bertujuan membangun sistem pendukung keputusan berbasis metode Additive Ratio Assessment (ARAS) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) guna membantu proses pemilihan anggota PPK secara sistematis dan adil. Metode penelitian menggunakan pendekatan rekayasa perangkat lunak dengan model pengembangan Waterfall, yang meliputi analisis kebutuhan, perancangan, implementasi, pengujian, dan evaluasi. Data kriteria seleksi diperoleh melalui observasi, wawancara, dan studi pustaka. Sistem dibangun menggunakan PHP dan MySQL, dengan ARAS dan TOPSIS sebagai dasar perhitungan multi-kriteria, mencakup indikator seperti pengalaman organisasi, kemampuan manajerial, integritas, pemahaman kepemiluan, serta keterampilan komunikasi.Hasil penelitian menunjukkan sistem mampu menghasilkan peringkat alternatif calon anggota PPK secara objektif. Uji kepuasan pengguna memperoleh skor 85,33% dengan kategori “sangat setuju”, menandakan penerimaan positif dari aspek fungsionalitas, kemudahan penggunaan, dan akurasi penilaian. Kesimpulannya, penerapan ARAS dan TOPSIS terbukti saling melengkapi dalam pemeringkatan calon PPK. Sistem ini menjadikan seleksi lebih transparan, efisien, dan kredibel, sekaligus berpotensi diterapkan pada rekrutmen sumber daya manusia di instansi pemerintahan lainnya.
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 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.
Analisis Seleksi Fitur  Menggunakan Metode ANOVA F-test dan Algoritma Random Forest Untuk Deteksi Diabetes Martono, Galih Hendro; Rismayati, Ria; Karor, Iptijanul
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.v1.5282

Abstract

Peningkatan level kadar glukosa darah yang melampaui batas normal merupakan ciri-ciri utama dari gangguan metabolisme yang dikenal sebagai diabetes mellitus, atau yang secara umum disebut penyakit kencing manis. Hal ini biasanya terjadi karena gangguan produksi atau fungsi insulin, baik secara absolut maupun relatif.  Diperkirakan  pada tahun 2030 diabetes akan menjadi penyebab kematian  terbesar ke-7 di dunia hal ini didasari laporan dari World Health Organization (WHO).  Ironisnya, sekitar 70% penderita diabetes tidak menyadari bahwa mereka mengidap penyakit ini, dan sekitar 25% telah mengalami komplikasi serius sebelum diagnosis ditegakkan. Oleh karena itu, deteksi dini serta manajemen risiko yang efektif sangat krusial untuk mencegah dampak kesehatan yang lebih berat. Pentingnya pemilihan fitur dalam meningkatkan akurasi prediksi diabetes adalah fokus penelitian ini. Metode seleksi fitur berbasis ANOVA F-test yang digabungkan dengan algoritma Random Forest dalam penyusunan model prediksi diabetes digunakan pada penelitian ini . Dataset yang digunakan terdiri dari 70.000 data dengan 33 atribut, yang kemudian diseleksi hingga diperoleh 13 fitur paling relevan berdasarkan nilai P-value < 0,05. Hasil evaluasi menunjukkan bahwa penerapan seleksi fitur secara signifikan meningkatkan performa model. Akurasi prediksi mencapai 73% saat menggunakan 5 fitur, meningkat menjadi 86% dengan 10 fitur, dan mencapai 90% ketika menggunakan 13 fitur. Temuan ini menggaris bawahi pentingnya proses seleksi fitur dalam pengembangan model prediktif penyakit diabetes, serta memberikan kontribusi penting dalam mendukung upaya deteksi dini dan pengelolaan risiko secara lebih optimal.
Penerapan Ensemble Learning dengan Hard Voting untuk Klasifikasi Customer Churn Astawa, Andhika rama putra; Martono, Galih Hendro; Mayadi
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.v1.5340

Abstract

Customer churn menjadi salah satu tantangan terbesar bagi perusahaan telekomunikasi karena berdampak langsung pada pendapatan dan keberlanjutan bisnis. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi churn dengan mengembangkan model ensemble learning berbasis Hard Voting Classifier yang menggabungkan tiga algoritma berbeda, yaitu Naïve Bayes, Random Forest, dan Nearest Centroid. Dataset pelanggan yang digunakan mencakup informasi demografis, perilaku penggunaan layanan, dan status churn, yang kemudian diproses melalui tahapan pembersihan data, seleksi fitur, normalisasi, serta teknik resampling SMOTE-Tomek untuk menyeimbangkan distribusi kelas. Pemilihan fitur dilakukan dengan metode Information Gain dan analisis korelasi, sehingga hanya atribut yang relevan digunakan dalam pemodelan. Hasil pengujian menunjukkan bahwa Hard Voting Classifier mampu mencapai akurasi sebesar 90% dengan nilai recall untuk kelas churn sebesar 81%, lebih tinggi dibandingkan Random Forest (78%), meskipun akurasi Random Forest lebih tinggi (95%). Nilai precision untuk kelas non-churn juga meningkat hingga 97%, menandakan model ini efektif mengurangi kesalahan dalam memprediksi pelanggan tetap. Temuan ini membuktikan bahwa pendekatan ensemble learning dengan base learner heterogen dapat memadukan keunggulan masing-masing algoritma untuk meningkatkan deteksi churn. Meski demikian, performa Hard Voting masih bergantung pada kualitas masing-masing classifier, sehingga optimasi hyperparameter dan eksplorasi kombinasi model lain direkomendasikan untuk penelitian selanjutnya. Hasil penelitian ini diharapkan dapat membantu perusahaan merumuskan strategi retensi pelanggan yang lebih tepat sasaran dan berkelanjutan. 
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.
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 : 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.
A Comparative Study of AutoSARIMAX and Long Short-Term Memory Models for Tourist Arrival Forecasting Saptarini, Dian; Saputri, Dian Syafitri Chani; Wardhana, Helna; Martono, Galih Hendro
Jurnal Varian Vol. 9 No. 1 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v9i1.5771

Abstract

This study aims to predict the number of tourist arrivals in West Nusa Tenggara (NTB) Province using two forecasting approaches: AutoRegressive Integrated Moving Average with Exogenous Variables (AutoSARIMAX) and Long Short-Term Memory (LSTM). The dataset was obtained from the Central Bureau of Statistics (BPS) of NTB and consists of international and domestic tourist arrivals and monthly inflation rates for the period 2014–2023. The research process includes data collection, preprocessing, model construction, and result evaluation. The AutoSARIMAX model is applied to capture linear relationships with exogenous variables, while LSTM is employed to model long-term nonlinear patterns. The findings reveal that the LSTM model achieved better forecasting performance, with a Mean Absolute Percentage Error (MAPE) of 2.65%, which is lower than AutoSARIMAX with 3.25%. Nevertheless, AutoSARIMAX provides valuable interpretability regarding the influence of inflation on tourist arrivals. Overall, the comparison between the two models indicates that LSTM is more effective for time-series forecasting of tourist arrivals, while AutoSARIMAX remains useful for analyzing causal relationships. These insights can support decision-making in tourism planning, particularly in anticipating fluctuations driven by economic and external factors.
Co-Authors Aguswandi, Lalu Heri Astawa, Andhika rama putra Azhari Azhari Azhari SN Dian Saptarini Dian Syafitri Chani Saputri Hasbullah Hasbullah Hasbullah Hasbullah Helna Wardhana Husain Husain Karor, Iptijanul Khabib Mustofa Lukmana, Aldi Mayadi Mundika, Eko Neny Sulistianingsih Ni Putu Sinta Dewi Ramadhan, Rahmat Adi Mulya Ria Rismayati Rismayati, Ria Tenia Wahyuningrum Triwijoyo, Bambang Krismono