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All Journal Speed - Sentra Penelitian Engineering dan Edukasi JIKO (Jurnal Informatika dan Komputer) Respati International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal Layanan Masyarakat (Journal of Public Service) Indonesian Journal of Data and Science SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen International Journal of Artificial Intelligence in Medical Issues
Sumiyatun
Fakultas Teknologi Informasi, Jurusan Sistem Informasi, Universitas Teknologi Digital Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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IMPLEMENTASI METODE PROMETHEE UNTUK PENILAIAN INDEKS KINERJA BERDASARKAN KOMPETENSI DOSEN Sumiyatun
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 17 No 3 (2019): September 2019
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v17i3.72

Abstract

Lecturers are both professional educators and researchers whose main tasks are transforming, developing and spreading knowledge, technology, and arts through education, research, and community service. This research produces a decision support system that can be used to determine the competence of lecturers at a tertiary institution. The use of Promethee as a multi-criteria analysis method is very helpful in determining the ranking of lecturer competencies implemented. Pedagogic competencies, 2. Professional competencies, 3. Personality Competencies and 4. Social Competencies. This system provides conclusions which consist of management in determining the ranking of competent lecturers
Performance Analysis of the Decision Tree Classification Algorithm on the Water Quality and Potability Dataset Zaky, Umar; Naswin, Ahmad; Sumiyatun, Sumiyatun; Murdiyanto, Aris Wahyu
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.113

Abstract

Ensuring water potability is paramount for public health and safety. This research aimed to assess the efficacy of the Decision Tree classification algorithm in predicting water potability using the Water Quality and Potability dataset. Employing a 5-fold cross-validation technique, the model showcased a moderate performance with an average accuracy of approximately 54.33%. While the Decision Tree provides a baseline and interpretable mechanism for classification, the results emphasize the need for further exploration using more intricate models or ensemble methods. This study contributes to the broader effort of leveraging machine learning techniques for water quality assessment and provides insights into the potential and limitations of such models in predicting water safety
Leveraging K-Nearest Neighbors for Enhanced Fruit Classification and Quality Assessment Iwan Sudipa, I Gede; Azdy, Rezania Agramanisti; Arfiani, Ika; Setiohardjo, Nicodemus Mardanus; Sumiyatun
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.125

Abstract

This study investigates the application of the K-Nearest Neighbors (KNN) algorithm for fruit classification and quality assessment, aiming to enhance agricultural practices through machine learning. Employing a comprehensive dataset that encapsulates various fruit attributes such as size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality, the research leverages a 5-fold cross-validation method to ensure the reliability and generalizability of the KNN model's performance. The findings reveal that the KNN algorithm demonstrates high accuracy, precision, recall, and F1-Score across all metrics, indicating its efficacy in classifying fruits and predicting their quality accurately. These results not only validate the algorithm's potential in agricultural applications but also align with existing research on machine learning's capability to tackle complex classification problems. The study's discussions extend to the practical implications of implementing a KNN-based model in the agricultural sector, highlighting the possibility of revolutionizing quality control and inventory management processes. Moreover, the research contributes to the field by confirming the hypothesis regarding the effectiveness of KNN in agricultural settings and lays the foundation for future explorations that could integrate multiple machine learning techniques for enhanced outcomes. Recommendations for subsequent studies include expanding the dataset and exploring algorithmic synergies, aiming to further the advancements in agricultural technology and machine learning applications.
GDSS MULTI KRITERIA PENENTUAN STRATEGI MARKETING TERBAIK PERGURUAN TINGGI Sumiyatun, Sumiyatun
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 9 No 1 (2019): Maret 2019
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (268.317 KB) | DOI: 10.33020/saintekom.v9i1.73

Abstract

Dunia pendidikan telah mengalami evolusi secara kontinyu. Salah satu faktor pemicunya adalah kompetisi antar perguruan tinggi yang semakin ketat. Oleh karena itu pemasaran menjadi unsur yang strategis dalam menjaga eksistensi perguruan tinggi. Dalam membuat keputusan pemilihan strategi marketing, terdapat beberapa kriteria yang perlu dipertimbangkan, diantaranya adalah biaya, waktu, tingkat pengaruh, capaian target dan lain sebagianya. Pada perguruan tinggi swasta pengambilan keputusan terkait strategi marketing tidak hanya ditentukan oleh bagian Humas saja, akan tetapi harus meminta pertimbangan dari pihak manajemen dan yayasan pemilik perguruan tinggi. Berdasarkan kompleknya permasalahan yang dihadapi, diperlukan sebuah sistem pendukung keputusan kelompok atau Group Decision Support System (GDSS) dalam menentukan strategi marketing pergurauan tinggi. Pada penelitian ini, penulis mengajukan GDSS menggunakan metode Analytical Hierarchy Process (AHP) yang mendukung model Multi Attribute Decision Making (MADM) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) sebagai model untuk pengambilan keputusan. Penggunaan GDSS pada penelitian ini bertujuan untuk mengakomodir penilaian lebih dari satu evaluator dan meningkatkan kualitas keputusan. Dengan demikian diharapkan penilaian yang dilakukan lebih obyektif, karena tidak dilakukan oleh satu pihak saja. Perangkat lunak yang dibangun dari hasil penelitian ini diharapkan mempermudah dan mempercepat dalam menentukan strategi marketing perguruan tinggi.
PENGEMBANGAN KONSEP MOBILE CITY MENUJU JOGJA SMART CITY Sumiyatun, Sumiyatun; Prayitna, Adiyuda
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 10 No 1 (2020): Maret 2020
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (312.742 KB) | DOI: 10.33020/saintekom.v10i1.108

Abstract

Yogyakarta selain terkenal sebagai kota perjuangan, kota pelajar, kota pariwisata jugadikenal sebagai kota budaya. Sebutan kota budaya untuk kota ini berkaitan erat dengan peninggalan-peninggalan budaya bernilai tinggi pada masa kejayaan kerajaan yang sampai kini masih tetap lestari. Meskipun berbagai layanan online sudah diterapkan, namun demikian sangat disayangkan karena belum terlihat adanya pengembangan e-Culture, padahal di Yogyakarta mempunyai nilai-nilai kebudayaan yang sangat potensial sehingga e-Culture ini sangat penting untuk kota yang berkemajuan dan berbudaya. Penelitian ini mengembangkan konsep mobile city dengan membangun aplikasi dengan teknologi mobile berbasis android untuk menginventarisir kebudayaan di kota Yogyakarta untuk mendukung konsep Jogja Smart City. Teknik pengumpulan data yang digunakan adalah observasi dan studi literature. Jenis data yang dikumpulkan adalah data primer dan sekunder yang bersifat kualitatif maupun kuantitatif. Data primer merupakan data yang diperoleh secara langsung dari OPD (Organisasi Perangkat Daerah) terkait dan masyarakat, sedangkan data sekunder diperoleh melalui data yang telah diteliti dan dikumpulkan oleh pihak lain yang berkaitan dengan objek penelitian. Hasil penelitian ini mengembangkan aplikasi mobile untuk menginventarisir kebudayaan di Kota Yogyakarta. Aplikasi ini juga dapat digunakan untuk menunjang promosi dan perwujudan konsep Jogja Smart City.
Classification Optimization of Skin Cancer Using the Adaboost Algorithm Sumiyatun; Maulidinnawati, Andi
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.86

Abstract

Early detection of melanoma skin cancer is crucial in improving prognosis and saving lives. This research aimed to optimize the classification of melanoma images using the Adaboost algorithm. Employing a dataset of 10,000 melanoma images, the study combined the Canny method for image segmentation, Hu Moments for feature extraction, and the Adaboost algorithm for classification. The 5-fold cross-validation results revealed an average accuracy of 61.52%. While the precision consistently surpassed recall, indicating the model's conservative nature in predicting positive cases. The outcomes align with previous research, emphasizing the challenges in melanoma classification. This study contributes to the domain by showcasing the potential and areas of improvement for machine learning in early melanoma detection. Future research is recommended to explore hybrid models and diversify data sources for enhanced robustness and generalizability.
Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset Azdy, Rezania Agramanisti; Syam, Rahmat Fuadi; Faizal, Edi; Sumiyatun, Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.96

Abstract

In this study, titled "Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset," we explore the effectiveness of the Bagging Meta-Estimator in diagnosing lung diseases, focusing on the challenges of imbalanced datasets. Utilizing a dataset segmented and characterized by Hu moments and encompassing categories of Normal, Bacterial Pneumonia, and Tuberculosis, the algorithm's performance was assessed through a 5-fold cross-validation. Results indicated moderate effectiveness with an average accuracy of 60.574%, precision of 60.749%, recall of 59.753%, and F1-Score of 59.416%, highlighting variable performance across folds. These findings suggest that while the Bagging Meta-Estimator has potential in medical imaging, further refinement is needed for consistent and reliable lung disease detection, especially in imbalanced datasets.
Penggunaan Metode SAW dan AHP dalam Penilaian Kinerja Pegawai untuk Pemberian Penghargaan Sudarmanto, Sudarmanto; Subiyantoro, Cuk; Tarigan, Thomas Edyson; Sumiyatun, Sumiyatun
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 22 No 3 (2024): September 2024
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v22i3.147

Abstract

Pengelolaan kinerja pegawai adalah aspek kunci dalam menjaga produktivitas dan kualitas kerja dalam institusi, dalam hal ini institusi pendidikan seperti UTDI. Dalam lingkungan kerja yang sangat kompetitif dan dinamis, penting bagi institusi untuk mengidentifikasi, mengakui, dan memberikan penghargaan kepada pegawai yang telah memberikan kontribusi yang luar biasa terhadap pencapaian tujuan. Untuk mencapai hal ini, seringkali menggunakan metode penilaian kinerja pegawai. Metode menentukan pemberian penghargaan kepada pegawai yang memiliki kinerja luar biasa, digunakan pendekatan gabungan yang terdiri dari metode Simple Additive Weighting (SAW) dan Analytic Hierarchy Process (AHP). Metode SAW digunakan untuk menghitung skor kinerja pegawai dengan memberikan bobot pada kriteria penilaian. Bobot ini didasarkan pada perbandingan pentingnya masing-masing kriteria. Penilaian kinerja menggunakan metode AHP dan SAW memberikan hasil yang sistematis dan dapat dipertanggungjawabkan. Visualisasi yang disajikan membantu memperkuat pemahaman terhadap hubungan antar kriteria dan bagaimana kinerja pegawai dinilai secara keseluruhan. Metode ini sangat bermanfaat dalam konteks penilaian multi-kriteria di berbagai organisasi.
EMPOWERING THE ROLE OF KARANG TARUNA IN ACHIEVING SMART VILLAGE DEVELOPMENT IN BANTUL REGENCY Sumiyatun; Hera Wasiati; Sudarmanto Sudarmanto; Aro Mulya Pratama; Feriawan Abdullah; Edi Faizal
Jurnal Layanan Masyarakat (Journal of Public Services) Vol. 10 No. 1 (2026): JURNAL LAYANAN MASYARAKAT
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Before this program was implemented, Karang Taruna Sekartaji in Pedukuhan Karanggayam faced concrete challenges in organizational governance and community security management. Administrative activities such as membership records, meeting attendance, and activity documentation were conducted manually, resulting in inconsistent data, limited accessibility, and difficulties in accountability. In addition, the absence of a structured digital security system reduced the effectiveness of community-based monitoring and reporting. This Community Partnership Program aimed to strengthen the role of Karang Taruna as a driver of smart village development through the digitalization of organizational administration and the implementation of an integrated digital security system. The program was conducted over eight months using a participatory approach through five stages: socialization, training, technology implementation, mentoring and evaluation, and sustainability planning. The results show significant improvements in organizational management with the adoption of a web-based Organizational Information System (OIS), as well as enhanced community security through the integration of a citizen reporting application, CCTV installation, and an Early Warning System (EWS).
A Comparative Study of Machine Learning Models for Stress Level Classification Using Social Media and Lifestyle Data M. Ikbal Siami; Aris Wahyu Murdiyanto; Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/r4d62a66

Abstract

The increasing use of social media and digital platforms has raised concerns regarding its potential relationship with sleep patterns, lifestyle behaviors, productivity, and psychological well-being. Stress is a common health-related issue that may be influenced by daily behavioral patterns, including screen time, social media usage, sleep duration, physical activity, and work or study habits. This study aims to develop and evaluate machine learning models for predicting stress levels based on non-invasive digital behavior and lifestyle indicators. The dataset used in this study consisted of 11,000 records with three stress level categories: Low, Medium, and High. The predictor variables included age, daily screen time, social media usage duration, sleep hours, exercise duration, study or work hours, productivity score, and the most frequently used social media platform. Several machine learning algorithms were evaluated, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Gradient Boosting. Model performance was assessed using accuracy, precision, recall, F1-score, confusion matrix analysis, and 5-fold stratified cross-validation. The experimental results showed that the overall classification performance was modest. The Decision Tree model achieved the best testing performance with an accuracy and macro F1-score of 0.3400, while Gradient Boosting achieved the highest cross-validation performance with a mean accuracy of 0.3480 and a mean macro F1-score of 0.3467. Feature importance analysis using Random Forest indicated that productivity score, sleep hours, study or work hours, social media hours, and daily screen time were the most influential variables. These findings suggest that digital behavior and lifestyle indicators may provide useful exploratory insights for stress-related analysis, although their predictive power remains limited. Therefore, the proposed approach is more suitable as an exploratory digital well-being assessment framework rather than a clinical diagnostic tool.
Co-Authors Aris Wahyu Murdiyanto Aro Mulya Pratama Cuk Subiyantoro Cuk Subiyantoro, Cuk Edi Faizal Edi Faizal Edi Iskandar Femi Dwi Astuti Feriawan Abdullah Hera Wasiati Hera Wasiati I Gede Iwan Sudipa Ika Arfiani M Ikbal Siami Maulidinnawati, Andi Naswin, Ahmad Nicodemus Mardanus Setiohardjo Prayitna, Adiyuda Pulut Suryati Retyanto Wardoyo Rezania Agramanisti Azdy, Rezania Agramanisti Sudarmanto Sudarmanto Sudarmanto Sudarmanto, Sudarmanto Syam, Rahmat Fuadi Syamsu Windarti Tarigan, Thomas Edyson Thomas Edyson Tarigan Thomas Edyson Tarigan Totok Suprawoto Umar Zaky Yagus Cahyadi Yasinta Marto Enga