Article Per Year (5 Year)
p-Index From 2021 - 2026
1.431
P-Index
This Author published in this journals
All Journal KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JOINT (Journal of Information Technology Jurnal Teknologi Informasi dan Komunikasi International Journal of Community Service Brilliance: Research of Artificial Intelligence Journal of Informatics and Electronics Engineering
Suhendri
Lecturer Of Politeknik Jatiluhur, Indonesia
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
Implementation Of Finite State Automata In A Laundry Perfume Vending Machine For Clothes And Carpets Desvia, Yessica Fara; Pratama, Febryawan Yuda; Suhendri, Suhendri
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 2 (2025): October
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i2.296

Abstract

Perfume is popular among various groups of people, including laundry fragrances. Laundry perfumes come in a variety of scen$ts, such as fruity, floral, a combination of fruit and floral, and woody aromas. These fragrances are typically applied during the final stage of the laundry process. Currently, customers receive their laundry with a randomly selected scent based on the availability at the laundry service, which means they cannot choose the fragrance they prefer. Therefore, a Vending Machine (VM) design is needed to allow customers to select their desired laundry perfume. The VM is designed using the Finite State Automata (FSA) approach, specifically the Non-Deterministic Finite Automata (NFA) type, as it can accommodate multiple conditions for a single option. The development of the NFA method involves stages such as business process analysis, state diagram creation, VM design, and system testing. The results of this study indicate that the implementation of this VM simplifies the process for customers to choose their preferred laundry perfume, ensuring that their laundry has a scent that matches their personal preferences.
AI-Based Market Intelligence as a Strategic Tool for Local MSME Development in West Java Suhendri; Dina Hidayat; Novasa Adiyani; Endang Ahmad; Fanissa Narita; Nico Irawan; Egi Sucianti
International Journal Of Community Service Vol. 6 No. 2 (2026): May 2026 ( Indonesia - Thailand - Malaysia - Philippines)
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijcs.v6i2.986

Abstract

Local Micro, Small, and Medium Enterprises (MSMEs) in Purwakarta, particularly ceramic craftsmen in Plered District, have strong potential to contribute to regional economic growth and enter broader export markets. However, they still face several major problems, including inconsistent product quality, limited design innovation, weak packaging standards, insufficient branding, limited digital marketing capacity, and low readiness to meet export market requirements. The gap identified in this activity is the limited integration between traditional ceramic production practices and modern market-oriented strategies, especially in quality control, product differentiation, digital promotion, and export preparation. Current developments show that MSME competitiveness is increasingly influenced by product innovation, digital marketing, branding, packaging quality, and export readiness. Therefore, this community service activity applied a participatory mentoring approach through field observation, discussion, consultation, and practical recommendations for ceramic craftsmen in Plered. The proposed solution focused on strengthening product quality, improving design and packaging, developing brand identity, optimizing digital marketing, and preparing MSMEs for export-oriented market access. The results indicate that the mentoring activity increased the awareness of ceramic craftsmen regarding product competitiveness, market standards, and the importance of continuous innovation. In conclusion, ceramic MSMEs in Plered have strong potential to improve their competitiveness in the export market when supported by consistent mentoring, quality standardization, strategic branding, digital promotion, and collaboration between higher education institutions, local industry, and community stakeholders.
Enhancing Business Decision-Making of Purwakarta MSMEs through AI-Based Market Analysis Warsono sudiro; Suhendri; Arlen Nawang Rachelia Arlen; Indri Yani Lasmini Indri
International Journal Of Community Service Vol. 6 No. 2 (2026): May 2026 ( Indonesia - Thailand - Malaysia - Philippines)
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijcs.v6i2.988

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in economic development; however, they face increasing challenges in making effective business decisions due to rapidly evolving market dynamics. Artificial Intelligence (AI) has emerged as a strategic tool to enhance decision-making processes through data-driven insights. This study aims to analyze the effect of AI-based market analysis on decision-making quality and its impact on business performance among MSMEs in Purwakarta. A quantitative approach was employed using survey data from 165 MSME owners, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that AI-based market analysis significantly improves decision-making quality (β = 0.66, p < 0.001), which subsequently enhances business performance (β = 0.61, p < 0.001). Furthermore, data readiness strengthens the effectiveness of AI utilization. These findings contribute to the literature on digital transformation and provide practical insights for MSMEs in adopting AI-driven strategies.
Lobster Growth Monitoring with AI-Based Computer Vision Using SVM and Neural Network Suhendri Suhendri; Ari Purno Wahyu Wibowo
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6210

Abstract

A modern agricultural and aquaculture technique today heavily relies on computer assistance. Computers aid in the analysis, identification, and regulation of feeding patterns, making the process more effective. For example, lobster farming is now predominantly conducted using pond-based methods, known as aquaculture, rather than sourcing lobsters from the wild. This is because lobsters are highly sensitive creatures, and failing to replicate their natural habitat can lead to crop failure. Several factors influence lobster farming conditions, including water quality, feed quantity, and lobster species. Another critical factor is disease outbreaks, which can spread rapidly due to the high lobster density in a single pond. Managing these conditions manually is impractical due to the large number of ponds and the need to replicate natural habitat conditions accurately. To address these challenges, a monitoring mechanism utilizing artificial intelligence (AI)-based image processing is implemented. AI methods can manipulate environmental conditions to closely resemble a lobster’s natural habitat by monitoring pH levels, determining gender, and assessing health status. Data accuracy is ensured using two algorithmic approaches. Experimental results show that the application is designed as a GUI with simple features, making it user-friendly for farmers and the general public. This application was tested using a sample of 200 lobsters, achieving a data accuracy rate of 95% with the SVM algorithm and 85% with the Neural Network algorithm. The application can identify lobster species, size, and potential diseases affecting them.
Peningkatan Kontras Citra Bawah Air Menggunakan Metode CLAHE (Contrast Limited Adaptive Histogram Equalization) Febryawan Yuda Pratama; Asep Sugiharto; Fanissa Narita; Suhendri Suhendri; Dikka Syahrial Wiguna
Journal of Informatics and Electronics Engineering Vol. 6 No. 01 (2026): Juni 2026
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v6i01.1618

Abstract

Kualitas citra bawah air umumnya buruk akibat penyerapan dan penyebaran cahaya di lingkungan perairan, yang menyebabkan keterbatasan jarak pandang kamera dan menghasilkan citra dengan kontras rendah, warna kabur, serta informasi visual yang berkurang. Maka dari itu penyerapan cahaya oleh air laut dan penyebaran cahaya oleh partikel kecil di lingkungan air laut telah menjadi sebuah rintangan dari penelitian citra bawah air dengan kamera. Hal ini dikarenakan memberikan dampak keterbatasan jarak pandang kamera dalam air laut. Citra atau gambar air bawah laut memang menjadi pekerjaan menantang karena kendala utamanya adalah kualitas gambar rusak karena penyerapan cahaya dan penyebaran cahaya. Peningkatan kualitas kontras citra pada dasarnya meningkatkan persepsi atau kemampuan menerjemahkan informasi gambar untuk manusia dan memberikan masukan “lebih baik” pada teknik pengolahan citra otomatis yang lain. Penelitian ini mengusulkan metode Contrast Limited Adaptive Histogram Equalization (CLAHE) untuk meningkatkan kontras citra bawah air khususnya pada ekosistem terumbu karang. Metode CLAHE bekerja dengan membagi citra ke dalam tile-tile kecil, membentuk histogram tiap region, memotong histogram dengan clip limit, mendistribusikan excess ke bagian lain, dan melakukan interpolasi bilinear antar tile. Eksperimen dilakukan terhadap 200 frame citra bawah air berukuran 1280×720 piksel menggunakan MATLAB. Hasil pengukuran menggunakan SURF feature points menunjukkan rata-rata PTS original sebesar 1.198,67 dan rata-rata PTS CLAHE sebesar 1.503,91. Pengujian statistik dengan SPSS v20 menghasilkan analisis varian image original sebesar 62.210,198 dan CLAHE sebesar 68.800,110 dengan nilai signifikansi 0,000 dan korelasi 0,999. Hasil ini membuktikan bahwa metode CLAHE secara signifikan meningkatkan kualitas kontras citra bawah air tanpa merusak informasi citra asli.
Co-Authors Abidin, Aa Zezen Zenal Adiyani, Novasa Aelani, Khoirida Ari Purno Wahyu Wibowo Arlen Nawang Rachelia Arlen Asep Sugiharto Bagus Alit Prasetyo Carkiman, Carkiman Chaniago, M Benny Dani Hamdani Desvia, Yessica Fara Dikka Syahrial Wiguna Dina Hidayat Egi Sucianti Endang Ahmad Fanissa Narita Febiannisa Utami Febryawan Yuda Pratama Febryawan Yuda Pratama Heri Heryono Heryono, Heri Indri Yani Lasmini Indri M Benny Chaniago Muhammad Abdul Mujib Muhammad Faizal Muharam, Fauzan Muhammad Nico Irawan Putri Rahayu Rahayu, Putri Ramadan, Wanda Siti Mardiana Siti Mardiana Usep Tatang Suryadi, Usep Tatang Warsono sudiro Yuli Murdianingsih, Yuli