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All Journal e-Journal Pustaka Kesehatan ASAS : Jurnal Hukum Ekonomi Syariah Journal of Agromedicine and Medical Sciences Indonesian Journal on Software Engineering (IJSE) Jurnal Pilar Nusa Mandiri Publica: Jurnal Pemikiran Administrasi Negara Journal of Intelligent Decision Support System (IDSS) Journal of Computer Networks, Architecture and High Performance Computing Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Salam (Islamic Economics Journal) Ilmu Komputer untuk Masyarakat Transformasi: Journal of Economics and Business Management Poltekita: Jurnal Ilmu Kesehatan Ta'allum: Jurnal Pendidikan Islam
Muhamad Hasan
Laboratorium Anatomi Fakultas Kedokteran Universitas Jember
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Social Sciences Other

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Classification of Banana Ripeness Based on Color and Texture Characteristics Ahmad Hafidzul Kahfi; Hasan, Muhamad; Riyan Latifahul Hasanah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.1985

Abstract

Banana is one of the most consumed fruits globally and is a rich source of vitamins, minerals and carbohydrates. With the many benefits that bananas have, many farmers cultivate this fruit. The problem that occurs when the harvest is produced on a large scale is the process of selecting bananas that are still unripe or ripe. Usually farmers carry out the selection process manually by visually identifying ripeness based on the color of the fruit skin. However, direct observation has several drawbacks such as subjectivity, takes a long time and is inaccurate. For this reason, we need a system that can help determine the maturity level of bananas automatically through a series of banana image processing processes. One way that can be used to determine the maturity level of bananas is by looking at the color and texture of the bananas. This study aims to classify the maturity level of bananas based on the color and texture characteristics of the banana image using the Gray Level Co-occurrence Matrix and K-Nearest Neighbor methods for the classification process. Based on the results of the research analysis that has been carried out, using the parameter k which has a value of 3 obtains very high accuracy.
Socio-Cultural Norms and Compliance with Cervical Cancer Screening: A Phenomenological Study among High-Risk Women Sumiaty, Sumiaty; Muhammad Nur Ali; Muhamad Hasan
Poltekita: Jurnal Ilmu Kesehatan Vol. 19 No. 3 (2025)
Publisher : Poltekkes Kemenkes Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33860/jik.v19i3.3901

Abstract

Background: Cervical cancer can be prevented through HPV vaccination and screening. However, the screening rate among women in Indonesia is still below 10%. Screening coverage in Palu City in 2024 is still below the national target (90%), reaching only 19.9%. Low compliance with screening is not only due to a lack of knowledge but also to social and cultural norms. This study aims to analyze social and cultural norms in cervical cancer screening compliance among high-risk women in Palu City. Methods: This study uses a naturalistic paradigm approach with a phenomenological research type. The participants in this study were women aged 30-50 years who met the established criteria. Data analysis is carried out through the stages of data reduction, thematic categorization, sociological interpretation, and theoretical synthesis using Durkheim's Collective Conscience theory, Bicchieri's Social Norms Theory, and Health Belief Model Theory. Results: The results of the study suggest that taboo norms are most dominant in cervical cancer screening compliance, with women who undergo reproductive organ examinations being considered shameful and rude. Folkways norms reveal that women only get checked when symptoms appear and self-medicate with traditional remedies. Moral values such as shame or husband's permission weaken the decision to undergo screening. Preventive knowledge and barriers such as stigma, shame, and lack of support weaken screening awareness. Conclusion: In conclusion, cervical cancer screening compliance is a social phenomenon shaped by taboo social norms, morality, customs, and symbolic power roles. Therefore, Socio-Normative Health Awareness Theory is a new intervention concept that reorients social norms into more effective empowerment tools, such as spousal consent, the role of religious leaders, customs, and morality as forces to strengthen awareness and participation in screening.
Butterfly species identification using glcm features and edge detection using KNN (K-Nearest Neighbor) and decision tree algorithm (C.45) Hasan, Muhamad; Riana, Dwiza; Merlina, Nita
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.341

Abstract

Butterflies are insects come from the kingdom Animalia, which are the Insecta class, the Lepidoptera order, and the sub-order of Rhopalocera. Butterflies can classified according to the patterns found on the butterfly's wings. Butterfly species have different patterns based on pigment, scale structure, and sunlight fall structure. The weakness of the human eye in specific the patterns in butterflies is the foundation in basis butterfly identification based on pattern recognition. This study used 3 butterfly species: Adonis, Black Hairstreak, and Gray Hairstreak. The butterfly dataset used was 150 which were obtained online. The pre-processing stage used segmentation and edge detection methods. The feature extraction stage used the Gray-level Co-occurrence Matrix (GLCM) method which extracted 8 shape and texture features including area, perimeter, metric, eccentricity, contrast, correlation, energy, and homogeneity. Classification phase used K-Nearest Neighbor (KNN) method with the values of k = 3, 5, 7, 9, 11, 13, 15, 17, and 19 as well as the Decision Tree method (C.45). The results of the identification of butterflies with the highest accuracy were obtained by the KNN Algorithm on the testing with a value of k = 3 of 93.33%, and the accuracy results using the Decision Tree method (C.45) is 84.44% while the results of identification using an application made using the GUI Matlab2017 with the KNN algorithm obtained an accuracy of 93.33% with a value of k= 3.
Co-Authors Ahmad Hafidzul Kahfi Anna Mukhayaroh, Anna Bintang Balele Cholis Abrori, Cholis Cicih Komariah Darodjat, Darodjat Dedy Chandra Hariyono Dedy Chandra Hariyono, Dedy Chandra Desie Dwi Wisudanti Djaya Mario Eri Surya Alfonsus Dwita Aryadina Rachmawati Dwiza Riana Erfan Efendi Erina Pane Fahrurrozi4, Nabil Ali Fauzi, Muhammad Yasir Hasbi Maulana Arsyad Heni Noviarita Indah Amin Sugiharti Jajang Jaya Purnama Kahfi, Ahmad Hafidzul Mattarima Mattarima Mayangky, Nissa Almira Muhammad Nur Ali Nita Merlina, Nita Panjaitan, Yogi Yosua Putra, Ananta Kusuma Riyan Latifahul Hasanah Riyan Latifahul Hasanah Rony Prasetyo Rony Prasetyo, Rony Sarah Kinan Andalusia Simanjuntak, Harapan Tua Ricky Fredy Siti Fauziah SRI RAHAYU Sriyanto Sriyanto Sumiaty Sumiaty Yani, Achmad Yedi Murdanu