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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) TEKMAPRO Journal of Industrial Engineering and Management CESS (Journal of Computer Engineering, System and Science) Briliant: Jurnal Riset dan Konseptual JOIV : International Journal on Informatics Visualization JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Kaizen : Management Systems and Industrial Engineering Journal IJID (International Journal on Informatics for Development) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Jurnal Sistem Komputer dan Informatika (JSON) Journal of Advanced in Information and Industrial Technology (JAIIT) Journal of Industrial Engineering and Operation Management (JIEOM) Jurnal Pengabdian Kepada Masyarakat Go-Integratif : Jurnal Teknik Sistem dan Industri Prosiding Konferensi Nasional PKM-CSR Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Manajemen NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics
Fauzi, Muhammad Dzulfikar
Institut Teknologi Telkom
Arts Humanities 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 Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Veterinary Other

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Simulasi Pengiriman Air Mineral Galon dengan Multi Depot Menggunakan Hill Climbing dan Algoritma A* Fauzi, Muhammad Dzulfikar; Hajar, Granita; Nur Rachmaniar, Desita; Hamim Zajuli al Faroby, Mohammad
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1170

Abstract

Air mineral sangat penting bagi kesehatan manusia karena berperan dalam hidrasi tubuh, menjaga keseimbangan cairan, dan mendukung fungsi organ serta sistem tubuh. Untuk memenuhi kebutuhan sehari-hari, distribusi dari depo air ke pelanggan harus mempertimbangkan rute tercepat agar biaya pengiriman efisien. Mencari rute terpendek dengan alokasi depo terbaik dengan tetap mempertimbangkan keterbatasan kapasitas masing-masing depo dan kendaraan merupakan tujuan dari penelitian ini. Algoritma Hill Climbing merupakan metode yang efektif untuk menentukan rute terdekat antar titik pengiriman. Selain itu, algoritma A* dapat digunakan untuk mencari rute optimal dengan menggunakan informasi tambahan (heuristik) yang mengarahkan pencarian ke jalur yang paling efisien. Berdasarkan hasil penelitian, 450,41 merupakan rute terjauh dengan menggunakan moda transportasi penjemputan dengan satu depo. Sedangkan untuk moda transportasi penjemputan dengan dua depo, jalur terpendek adalah 257,62; sedangkan untuk menggunakan sepeda motor atau kereta api, jalur terpendek adalah 271,75.
A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets Adhinata, Faisal Dharma; Ramadhan, Nur Ghaniaviyanto; Fauzi, Muhammad Dzulfikar; Tanjung, Nia Annisa Ferani
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1164

Abstract

Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies
Penentuan Pembukaan Gerai dengan Menggunakan Analytic Hierarchy Process Empat Layer Muhammad Dzulfikar Fauzi; Granita Hajar; Abdul Kholik
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 4 (2022): Juni 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4289

Abstract

In the neighborhood, there is a slew of flavored drink shops. Bubble drinks, sometimes known as "boba drinks," are currently popular across various demographics, particularly among teenagers and young people. The sweet taste of the drink and the range of options drive demand for this boba drink, which is growing increasingly popular. In addition to its presentation, this beverage producer offers customization options for beverage taste, sugar level, drink glass size, and even ice cube quantity to satisfy consumer preferences. Several criteria, such as pricing, purchasing power, and location, must be considered while deciding on store opening These elements can still be subdivided. Therefore the AHP (Analytical Hierarchy Process) method is appropriate for determining store openings. The findings of this study demonstrate that, among numerous alternative locations, location G has the highest value, with a weight of 0.2236.
A Hybrid DenseNet201-SVM for Robust Weed and Potato Plant Classification Muhammad Dzulfikar Fauzi; Faisal Dharma Adhinata; Nur Ghaniaviyanto Ramadhan; Nia Annisa Ferani Tanjung
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.23886

Abstract

Potato plant growth needs to be protected from weeds that grow around it. Currently, the manual spraying of pesticides by farmers is not only precise on weeds but also on cultivated plants. Therefore, we need an intelligent system that can appropriately classify potato plants and weeds. The research contribution combines feature extraction and appropriate classification methods to obtain optimal accuracy. In addition, the small amount of data also contributes to this research. In this research, it is proposed to use a combination of feature extraction using deep learning techniques and classification using machine learning. We use the feature extraction method with the DenseNet201 model because this study's data is not too much. Complex vectors from DenseNet201 were reduced using Principal Component Analysis (PCA). Then we classified it with the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classification methods. The experimental results show that the PCA method can reduce the complexity of high-dimensional features into 2 and 3 dimensions. The average of the best classification results using SVM was obtained with a 3-dimensional PCA configuration, but on the contrary, using KNN obtained the best results in a 2-dimensional PCA configuration. The results showed 100% accuracy on the DenseNet201-SVM hybrid. The SVM kernel configuration used is a linear kernel. The results of this study can be an insight into an accurate classification method for separating weeds and potatoes so that agricultural technology can apply this method for classification.
Co-Authors Abdul Kholik Agfianto Eko Putra Agung Fatwanto Agung Fatwanto Ahmad Wali Satria Bahari Johan Aprilliyanto, Tofik Arda Erico Yuda Arda Erico Yuda Ardian Yusuf Wicaksono Arifin, Hendra Dea Arliyanti Nurdin Arliyanti Nurdin , S.T., M.T Asyhab, Naufal Aulia Faqih Rifa’i Bambang Sugiantoro Bunga Pasha Maulidina Chasandra Puspitasari Desita Nur Rachmaniar Faisal Dharma Adhinata Granita Hajar Haq, Azizi Khoirul Hendra Dea Arifin Heni Hapsari Heri Setiawan Ida Ayu Putu Sri Widnyani Islami, Mega Cattleya P.A Kasliono Kasliono Khurin 'ien Mukhoyyaroh Laksono Kurnianggoro Maria Ulfah Siregar Matteo Ibastian Mohammad Hamim Zajuli Al Faroby Montolalu, Billy Muhammad Akid Musyafa Muhammad Alfian Jauhari Muhammad Mustakim, Muhammad Muhammad Taufiq Nuruzzaman Musyafa, Muhammad Akid Nia Annisa Ferani Tanjung Nicko Nur Rakhmaddian, Nicko Nur Nisa Isrofi Nur Ghaniaviyanto Ramadhan Nur Rachmaniar, Desita Oktavianita, Annisa Dwi Pertiwi, Kharisma Monika Dian Pima Hani Safitri Rachmadi, Andry Rahmaani, Amalia Ratna Rahmawati, Feri Rizky Fenaldo Maulana Rizky Novera Harnaningrum Shofwatul ‘Uyun son, Sumarsono Sri Lestari Sunu Pinasthika Fajar Suryana Wijaya T. Rasmana , Susijanto Wahyono Wahyono Wahyu Andy Prastyabudi Wahyu Sri Mulyani Yulianto, Ilham