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All Journal International Journal of Advances in Intelligent Informatics MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sistem Cerdas Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Teknologi Informasi dan Multimedia Jurnal Varian Jurnal Computer Science and Information Technology (CoSciTech) Reputasi: Jurnal Rekayasa Perangkat Lunak J-Intech (Journal of Information and Technology) Jurnal Minfo Polgan (JMP) 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 Jurnal Masyarakat Madani Indonesia Jurnal Elektronika dan Teknik Informatika Terapan Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL SmartComp Edu Elektrika Journal Jurnal Pengabdian Pada Masyarakat IPTEKS Prosiding Seminar Nasional CORISINDO Journal of Information Technology
Galih Hendro Martono
Universitas Bumigora, Mataram, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering 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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ANALISIS SENTIMEN KINERJA TENAGA MEDIS INDONESIA MENGGUNAKAN MODELING ROBERTA DAN METODE MACHINE LEARNING Husaini, Rahayun Amrullah; Priyanto, Dadang; Hendro Martono, Galih
Edu Elektrika Journal Vol. 13 No. 1 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/eduel.v13i1.22163

Abstract

The development of information technology has led to the emergence of various social media applications, such as X, which allow users to share information and opinions. However, social media can also function as a platform for the spread of hoaxes and hate speech. One of the challenges faced is determining whether user comments are negative, neutral, or positive through sentiment analysis. This study aims to compare the performance of various classification algorithms in sentiment analysis of medical personnel services in X, using the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithms with RoBERTa model-based labeling. Data was collected through the tweet-harvest library and processed using the Python programming language. The results showed a significant increase in accuracy, with the model able to classify public opinion into positive, negative, or neutral categories. The SVM model achieved the highest accuracy of 91.8%, outperforming other models such as Random Forest and Naïve Bayes, and provided insight into sentiment towards government health services. These findings provide valuable insights for policymakers in improving the provision of health services and managing public perceptions of medical personnel.
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.
Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification Neny Sulistianingsih; Galih Hendro Martono
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.
Segmentasi Hotel di Lombok Menggunakan Metode Klasterisasi Berbasis Harga, Fasilitas, dan Jarak Lokasi Eldy Waliyamursida; Dadang Priyanto; Galih Hendro Martono
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.722

Abstract

Lombok is one of Indonesia's premier tourist destinations, experiencing significant growth in the tourism sector. The increasing number of visitors has directly impacted the hospitality industry, resulting in a wide variety of hotels with diverse characteristics based on price, rating, and customer reviews. This diversity poses a challenge in effectively understanding hotel market segmentation. This study aims to cluster hotels in Lombok using clustering techniques to gain deeper insights into hotel segmentation patterns. The research employs the K-Means Clustering algorithm within the CRISP-DM framework, which includes the phases of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The dataset comprises attributes such as nightly price, hotel rating, and the number of reviews, all collected from online platforms. The effectiveness of the clustering process is evaluated using the Silhouette Score metric. The results show that the K-Means algorithm delivers the best performance, with a Silhouette Score of 0.9042 (90%), indicating well-defined and distinct clusters. Therefore, K-Means Clustering is recommended as the most effective method for grouping hotels based on the attributes used in this study. This research provides valuable insights into hotel segmentation patterns in Lombok and can serve as a reference for hospitality industry stakeholders in formulating more targeted marketing strategies and business decisions. Future research may consider incorporating additional attributes such as geographic location and tourist seasons to enhance the clustering quality.
Co-Authors Aguswandi, Lalu Heri Astawa, Andhika rama putra Azhari Azhari Azhari SN Baiq Anna Naura Nazifa Dadang Priyanto Dian Saptarini Dian Syafitri Chani Saputri Eldy Waliyamursida Hairani Hairani Hasbullah Hasbullah Hasbullah Hasbullah Helna Wardhana Helna Wardhana Wardhana Husain Husain Husain Husain Husaini, Rahayun Amrullah Ismarmiaty Janji Armanda Fabian Karor, Iptijanul Khabib Mustofa Lukmana, Aldi M Rizki Mayadi Muhammad Fathoni Mundika, Eko Neny Suliastianingsih Neny Sulistianingsih Neny Sulistianingsih Neny Sulistianingsih Neny Sulistianingsih Ni Putu Sinta Dewi Nindya Alifia Khumaira Nyoman Gita Tri Aditya Ramadhan, Rahmat Adi Mulya Ria Rismayati Rinda Fitriana Azmi Rismayati, Ria Syahrir, Moch. Tenia Wahyuningrum Triwijoyo, Bambang Krismono