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All Journal Community Development Journal: Jurnal Pengabdian Masyarakat Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Journal of Applied Business and Technology Eastasouth Journal of Impactive Community Services Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Statistika Industri dan Komputasi PROTON : Jurnal Ilmu-Ilmu Teknik Mesin Socratika
Sholeh Muhammad
Department Of Computer Science, Faculty Of Science And Information Technology, Universitas AKPRIND Indonesia, Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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KLASIFIKASI TINGKAT KEMATANGAN BUAH PISANG DENGAN ALGORITMA CNN  Amir Hamzah; Renna Yanwastika Ariyana; Untung Joko Basuki; Muhammad Sholeh; Bagas Tri Basgoro
Jurnal DutaCom Vol 19 No 1
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/xmqa2d79

Abstract

Bananas are the most widely produced fruit in Indonesia, which is around 9.69 million tons in 2024. With this amount of data, bananas have quite promising economic value. The production of large quantities of bananas according to the previous data needs to be carried out a rapid distribution process, because the time the bananas after harvesting may only last about 5 to 7 days in normal temperatures before the fruit rotting process will finally occur. For this reason, it is necessary to carry out a quick banana classification process, the process is carried out automatically using a machine. This study elaborates on the capabilities of the CNN algorithm  in the classification of bananas. Dataset was taken from Keagle's open source as many as 3000 image data. In their classification, researchers divided bananas into three levels of fruit ripeness, namely raw, ripe, and overripe. The study used a CNN model  consisting of several layers consisting of a 2D convolutional layer (Conv2D), a 2D pooling layer (MaxPooling2D), a flatten layer, a fully connected layer (Dense), and a Dropout layer. After the model creation process is complete, the model will be tested for accuracy with  the Confusion matrix method. In the 3rd experiment the model produced the highest level of accuracy in its trials, with 300 test images resulting in 290 correctly predicted images so that the accuracy reached 97%. From the results  of deploying using the website interface using flask, it was found that the classification had an accuracy of above 95% so it was good enough to be used as a prototype for classification engine applications.
The Effect of TAM Cognitive Factors on Usage Intention, Moderated by Organizational Support Bikorin Bikorin; Muhammad Sholeh; Suwanto Raharjo; Anggun Sulistyowati Sulistyowati
Socratika: Journal of Progressive Education and Social Inquiry Vol. 3 No. 2 (2026)
Publisher : South Sulawesi Education Development

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58230/socratika.v3i2.688

Abstract

Digital transformation has become the approach adopted to improve performance. Learning Management Systems (LMS) bridge the learning gap between educators and learners. The Technology Acceptance Model (TAM) is insufficient for analysis; the Object-Oriented Systems (OST) approach complements the mechanisms of technological intervention. This study aims to analyze the moderating effect of organizational support, mediated by TAM, on the intention to adopt technology. This study employed a quantitative approach using an explanatory case study method. The study sample consisted of students at private universities in Yogyakarta who use an LMS. The sample size was 80 respondents, and data were collected via an online survey. SEM-PLS was selected as the analytical method. Data analysis shows that one hypothesis was accepted and two hypotheses were rejected. Perceived ease of use has a positive effect on perceived usefulness in using an LMS. Neither the effect of perceived ease of use nor that of perceived usefulness on behavioral intention via organizational support is statistically significant Theoretically, TAM suggests that ease of use functions both as a cognitive factor and as a direct prerequisite. Ease of use increases the intention to use the system, but institutional support does not always strengthen or weaken this relationship. Institutional support is unable to alter the beliefs regarding benefits that drive behavioral intentions. Its theoretical contribution lies in the integration of technology acceptance and organizational institutional support. In practice, universities and system developers collaborate to produce advanced technologies that accommodate needs resources.
Co-Authors Aji Pranoto Aji Pranoto Akhmad Fadjeri Akhmad Fadjeri Amir Hamzah Anggun Sulistyowati Arie Noor Rakhman Aulia Rahmawati Bagas Tri Basgoro Bikorin Dimas Taufiq Ridlo Dyah Indriyaningsih Septeri Endang Widayati Erma Susanti Erna Kumalasari Nurnawati Fajar Yulianto Prabowo Fivry Wellda Maulana Heny Budi Setyorini, Heny Budi I Gusti Gde Badrawada Idam Wahyudi Inggit Fatika Joko Waluyo Kris Suryowati, Kris Limpat Wibowo Aji Masrur Alatas Noviana Pratiwi Putri Indriani Rahmat Joko Nugroho Rasyid Zuhdi Renna Yanwastika Ariyana Renna Yanwastika Ariyana Risdianto Hermawan Septiono Eko Bawono Suparmi Setyowati Rahayu Suwanto Raharjo Syafriyudin Taufik Hidayat Tegar Satria Iman Saputra Toto Rusianto Untung Joko Basuki