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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Pertanian Indonesia Agromet MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah Jurnal Pustakawan Indonesia FORUM STATISTIKA DAN KOMPUTASI Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Pembangunan Wilayah dan Kota Agrikultura Jurnal Keteknikan Pertanian Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika Forum Agribisnis SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) International Journal of Advances in Intelligent Informatics Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Widyariset JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Informatika Pertanian Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang Jurnal ULTIMATICS CYBERNETICS BHUMI: Jurnal Agraria dan Pertanahan Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Aptisi Transactions on Technopreneurship (ATT) JURNAL METEOROLOGI DAN GEOFISIKA Building of Informatics, Technology and Science Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Jurnal Tanah dan Iklim Widyariset Aiti: Jurnal Teknologi Informasi Computer Science (CO-SCIENCE) Jurnal Pustakawan Indonesia Makara Journal of Science Jurnal Ilmu Kehutanan Eduvest - Journal of Universal Studies J-Icon : Jurnal Komputer dan Informatika Jurnal Sistem Informasi

Agus Buono

Department Of Computer Science, Faculty Of Mathematics And Natural Science, Bogor Agricultural University, Jalan Meranti, Kampus IPB Darmaga, Bogor 16680,

Author-ID : 221408

Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Other

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Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Buono, Agus; Priandana, Karlisa; Paruhum Silalahi, Bib; Sri Hendrastuti, Elisabeth
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.

Prediction of Duration of Dry Bamboo Leaf Counting Using Fuzzy Logic Marcelita, Faldiena; Mindara, Gema Parasti; Noviyanti, Inna; Sholihah, Walidatush; Buono, Agus
Eduvest - Journal of Universal Studies Vol. 3 No. 12 (2023): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v3i12.967

Dry bamboo leaves are selected as a planting medium for ornamental plants to enhance nutrient content and improve soil drainage. Before being processed into fertilizer, dry bamboo leaves need to be shredded first. The leaf counting process for planting ornamental plants in villages still relies on manual methods such as using knives and scissors, which can be time-consuming and less effective. In response to this issue, a device for shredding dry bamboo leaves has been developed to improve efficiency and facilitate the leaf counting process. The innovation created is a household-scale dry bamboo leaf shredder that is more affordable and easy to mobilize due to its compact dimensions and lighter weight. The device is also easy to operate, as the shredding occurs when it is closed, and the process stops when it is opened, ensuring safe use for various users. The manual control of bamboo leaf weight and counting duration results in inconsistent shredding outcomes. Therefore, development has been carried out on the dry bamboo leaf shredder with artificial intelligence capabilities, specifically using fuzzy logic to automate the counting duration based on the size and weight of the bamboo leaves being inputted.

Modified Q-Learning Algorithm for Mobile Robot Real-Time Path Planning using Reduced States Hidayat; Buono, Agus; Priandana, Karlisa; Wahjuni, Sri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4949

Path planning is an essential algorithm in any autonomous mobile robot, including agricultural robots. One of the reinforcement learning methods that can be used for mobile robot path planning is the Q-Learning algorithm. However, the conventional Q-learning method explores all possible robot states in order to find the most optimum path. Thus, this method requires extensive computational cost especially when there are considerable grids to be computed. This study modified the original Q-Learning algorithm by removing the impassable area, so that these areas are not considered as grids to be computed. This modified Q-Learning method was simulated as path finding algorithm for autonomous mobile robot operated at the Agribusiness and Technology Park (ATP), IPB University. Two simulations were conducted to compare the original Q-Learning method and the modified Q-Learning method. The simulation results showed that the state reductions in the modified Q-Learning method can lower the computation cost to 50.71% from the computation cost of the original Q-Learning method, that is, an average computation time of 25.74s as compared to 50.75s, respectively. Both methods produce similar number of states as the robot’s optimal path, i.e. 56 states, based on the reward obtained by the robot while selecting the path. However, the modified Q-Learning algorithm is capable of finding the path to the destination point with a minimum learning rate parameter value of 0.2 when the discount factor value is 0.9.

PENGELOLAAN COLD STORAGE IKAN DALAM PERSPEKTIF EKONOMI KELEMBAGAAN BARU Suharno, Suharno; Firdaus, Nova; Suharno; Buono, Agus
Forum Agribisnis Vol. 14 No. 2 (2024): FA VOL 14 NO 2 SEPTEMBER 2024
Publisher : Magister Science of Agribusiness, Department of Agribusiness, FEM-IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/fagb.14.2.1-15

Cold storage yang dibangun Kementerian Kelautan dan Perikanan (KKP) di berbagai daerah sebagai salah satu implementasi kebijakan Sistem Logistik Ikan Nasional (SLIN) yang bertujuan untuk menjaga ketersediaan ikan untuk bahan baku industri dan konsumsi dalam negeri. Cold storage 1.000 ton didirikan untuk menjadi role model sarana buffer stock milik pemerintah di Wilayah Jakarta namun dalam implementasinya masih mengalami kendala karena pemanfaatannya belum optimal dan belum menarik minat pelaku perikanan dalam cakupan yang lebih luas. Tujuan penelitian adalah mengkaji sistem kelembagaan yang diterapkan dalam pengelolaan cold storage 1.000 ton dari perspektif bisnis. Penelitian menggunakan metode studi kasus di cold storage pemerintah yaitu cold storage berkapasitas 1.000 ton yang terletak di Muara Baru, Jakarta Utara. Analisis kelembagaan pengelolaan cold storage dilakukan secara kualitatif deskriptif dengan pendekatan teori Ekonomi Kelembagaan Baru. Output analisis yang diharapkan berupa penilaian terhadap kelembagaan yang berjalan apakah telah sesuai dengan prinsip-prinsip ekonomi kelembagaan baru dan teori yang mendukung lainnya. Hasil penelitian menunjukkan bahwa kelembagaan cold storage 1.000 ton masih membutuhkan peningkatan pengelolaan. Aspek yang paling berkontribusi terhadap kurangnya kinerja bisnis di cold storage tersebut adalah property right dan flexibility and adaptability. Implikasi yang dapat diberikan adalah perbaikan kelembagaan melalui 1) pengembangan model bisnis yang lebih customize dengan kebutuhan pengguna, 2) pembentukan badan atau lembaga yang lebih otoritatif dalam layanan publik, 3) penyelarasan insentif dan membangun kerja sama dengan cakupan pengguna yang lebih luas, 4) penerapan standar prosedur yang diimbangi komitmen kepatuhan dari stakeholders dan kontrol yang kuat, 5) peningkatan kecepatan dan fleksibilitas layanan serta 6) penetapan harga yang kompetitif.

Perancangan Prototipe Sistem Manajemen Pengetahuan Antar Universitas (Studi Kasus IPB Dan UNPAK) Ibrahim, Firmansyah; Hermadi, Irman; Buono, Agus
Jurnal Pustakawan Indonesia Vol. 14 No. 2 (2015): Jurnal Pustakawan Indonesia
Publisher : Perpustakaan IPB

The rapid development of knowledge encourages universities to collaborate on their knowledge in specific expertise field to create an equitable distribution of knowledge. Program of computer science at Bogor Agriculture University (IPB) is a superior expertise in the field of agriculture while Pakuan University (UNPAK) is superior expertise in the field of electronics. The aim of this study was to design a prototype of knowledge management system as the knowledge sharing for learning of inter-universities using Knowledge Management System Life Cycle (KMSLC) method. The study result was Joomla one kind of Content Management System (CMS) can be used to share knowledge in the form of discussion forums and combining various info from IPB and UNPAK in one interface including news, announcements, agenda, and social media. This CMS also collaborate with applications Electronic Learning System (ELS) Moodle and Hyper Text Markup Language (HTML). ELS Moodle serves as an application in learning of inter-university with the single sign concept which was run in a single interface, while HTML serves as a search engine knowledge by generating external link that combines 2 KMS from IPB and UNPAK into one interface. The conclusion was the knowledge sharing for learning of inter-university can be done with the design of knowledge management system through collaboration three different systems.Keywords: CMS Joomla, ELS Moodle, HTML search engine, KMSLC, Knowledge Management Systems, Prototype

Pengembangan Sistem Pakar Identifikasi Awal Penyakit Kedelai Dengan Pendekatan Naïve Bayes Berbasis Android Astuti, Indah Puji; Hermadi, Irman; Buono, Agus; Mutaqin, Kikin H.
Jurnal Pustakawan Indonesia Vol. 14 No. 2 (2015): Jurnal Pustakawan Indonesia
Publisher : Perpustakaan IPB

Pengidentifikasian penyakit kedelai secara dini menjadi salah satu cara untuk meningkatan angka produktifitas kedelai. Jumlah pakar penyakit kedelai yang masih relatif sedikit apalagi di daerah pedesaan membuat ketergantungan atas keberadaan seorang pakar penyakit kedelai sangatlah tinggi terutama bagi para pemula di bidang pertanian. Suatu sistem pakar menjadi salah satu solusi yang dapat dijadikan sarana untuk berkonsultasi tentang penyakit kedelai layaknya seorang pakar. Sistem yang diimplementasikan dalam basis Android akan lebih mudah digunakan di manapun dan kapanpun tanpa harus bertemu dengan pakar karena kesempatan dan waktu pakar yang tidak mudah untuk ditemui setiap saat. Tujuan penelitian ini adalah untuk mengembangkan sistem pakar identifikasi awal penyakit kedelai dengan mengadopsi metode Expert System Development Life Cycle (ESDLC) untuk tahapan pengembangan sistem dan pendekatan Naïve Bayes sebagai metode inferensinya. Hasil penelitian ini berupa prototype sistem pakar XSIDS yang terdiri dari enam modul utama yaitu modul pengetahuan tentang kedelai, kebijakan pemerintah, konsultasi, tentang kami, tentang XSIDS dan note.Kata Kunci : Android, ESDLC, Naïve Bayes, Sistem Pakar, Penyakit Tanaman Kedelai

Enhancing MSME Digital Marketing through Public-Private Partnerships with Fuzzy AHP Bahukeling, Trukan Sri; Suroso, Arif Imam; Buono, Agus; Nurhayati, Popong
Aptisi Transactions On Technopreneurship (ATT) Vol 8 No 1 (2026): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v8i1.667

Public Private Partnership (PPP) is a contractual arrangement between government and private institutions to share resources, tools, and expertise for delivering public services. In Indonesia, PPPs have been widely applied in infrastructure sectors, but little attention has been given to their role in supporting micro, small, and medium enterprises (MSMEs) in adopting digital marketing. This study aims to build a PPP institutional scenario for implementing digital marketing in MSMEs and to formulate a priority strategy for strengthening PPP-based digital marketing policy. A mixed-method approach was employed, combining scenario planning with the Fuzzy Analytical Hierarchy Process (FAHP). Data were collected from 17 experts through in-depth interviews and analyzed using scenario modeling and fuzzy weighting techniques. The findings indicate that developing institutional guidelines, establishing a task force, fostering commitment, and creating PPP patterns are key strategies to enhance MSMEs’ digital adoption. The results highlight the importance of government investment in digital infrastructure, training programs, and supportive regulations. This research provides practical contributions for policymakers in strengthening PPP-based digital ecosystems and academic contributions by advancing scenario-based decision-making for sustainable MSME development.

Penentuan Kerapatan Massa Tajuk Pohon Decurrent Berdasarkan Analisis Keragaan Fisik Aini, Siti Churotul; Ulfa Adzkia; Agus Buono; Siregar, Iskandar Zulkarnaen; Lina Karlinasari
Jurnal Ilmu Kehutanan Vol 20 No 1 (2026): March
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jik.v20i1.22134

Self-weight calculation was identified as an essential step in tree structural analysis. In practice, this calculation included estimating stem weight based on wood density, while crown density was expressed as a percentage of crown area. However, previous research had not provided explicit methodologies for determining crown density as a function of crown mass and volume. Therefore, this research aimed to establish a proper method and corresponding conversion factors for translating area-based crown density estimates to those derived from crown weight and volume. A total of 15 healthy decurrent trees were selected to assess crown volume and density through physical attribute analysis. The new crown area density was calculated after pruning, where all pruned crown biomass was collected and weighed to determine crown mass. Mathematical analyses were developed to convert crown density values. The results showed that decurrent trees had a mean crown density of approximately 2.95 kg/m3, exceeding the value reported in reference research for excurrent trees (1.9 kg/m3). Since this research focused on tropical tree species, the results could serve as a reference for subsequent research on tropical tree structural characteristics.

EVALUATION OF ANN- LEVENBERG MARQUARDT MODELS FOR FAULT DETECTION IN SMART FARMING SYSTEM Luh Kesuma Wardhani; Agus Buono; Sri Wahjuni; Muhamad Syukur
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7481

Sensor readings in open field monitoring systems are influenced by disruptions, degradation, and operational unreliability. These conditions may result in inaccurate data and unreliable system decisions. However, existing studies focus on detection accuracy and rarely examine the trade-off between detection performance and computational efficiency of Artificial Neural Networks trained using the Levenberg–Marquardt algorithm (ANN–LM) in smart farming environments. This study evaluates the fault-detection capability of ANN–LM for soil moisture sensor readings by analyzing both detection performance (accuracy, precision, recall, and F1-score) and computational efficiency (execution time, CPU usage, and memory consumption), thereby addressing the trade-off between performance and efficiency. Baseline data, hypothetical dataset that represent the soil moisture reading from a smart chilli pepper farming system in normal operating conditions, were used to generate fault-injected datasets representing four common faults: drift, bias, spike, and malfunction. The ANN–LM model was evaluated under five fault-detection scenarios with different network architectures. Model performance was evaluated using accuracy, precision, recall, and F1-score, while computational cost was assessed through execution time, CPU usage, and memory usage. The results show that ANN–LM achieves an accuracy of 0.996–0.999, precision of 1.000, recall of 0.987–1.000, and F1-scores of 0.992–1.000 across all scenarios. Simple ANN architectures give accuracy of 0.997 with reduced execution time (33.74 seconds) and lower CPU usage (50.50%) compared to more complex architectures that require 591.88 seconds and 78.40% CPU usage. Therefore, these results indicate point out that ANN–LM is suitable for smart agricultural systems under resource-constrained conditions.

COMPARATIVE EVALUATION OF YOLOV5–YOLOV11 MODELS FOR DETECTING NUTRIENT DEFICIENCY IN CHILI SEEDLINGS Rangga Pebrianto Rangga; Agus Buono; Heru Sukoco; Aziz Kustiyo; Muhamad Syukur
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.8263

Nutrient deficiencies during the seedling stage of chili plants can reduce crop productivity, while conventional identification methods remain subjective and costly. This study compares YOLOv5 to YOLOv11 object detection models for detecting nutrient deficiency symptoms in Bonita chili seedling leaves, including complete nutrition, nitrogen deficiency, phosphorus deficiency, potassium deficiency, and NPK deficiency. The final dataset comprised 4,173 images derived from 1,739 original annotated leaf images through controlled dataset preparation, including split-before-augmentation, laboratory validation of nutrient conditions, and expert-reviewed labeling. All YOLO models were trained and evaluated using the same dataset partition and comparable experimental settings. Performance was assessed using mAP@0.5, computational complexity (FLOPs), inference speed, and model size. The results show that all evaluated models achieved high detection performance, with differences mainly appearing in computational efficiency and the balance between accuracy and speed. YOLOv10s and YOLOv11s obtained the highest mAP@0.5 in this experiment, whereas YOLOv8s showed a competitive balance between accuracy, inference speed, and model compactness. These findings indicate that recent YOLO developments are promising for fine-grained nutrient deficiency detection in computer vision–based precision agriculture.

Co-Authors Ade Fruandta Adi Rakhman Aditya Cipta Raharja Agung Prajuhana Putra Aini, Siti Churotul Akhmad Faqih Alif Kurniawan Alvin Fatikhunnada Anang Kurnia Angga Wahyu Pratama Aries Maesya Arif Imam Suroso Arini Aha Pekuwali Arini Pekuwali Astuti, Indah Puji Atik Pawestri Sulistyo Aziz Kustiyo Aziz Kustiyo Aziz Rahmad Bahukeling, Trukan Sri Benyamin Kusumoputro Bib Paruhum Silalahi Budi Nugroho Cece Sumantri Dhany Nugraha Ramdhany Dian Kartika Utami Edi Santosa Ekowati Handharyani Elisabeth Sri Hendrastuti Endang Purnama Giri Erliza Hambali Erliza Noor Ernan Rustiadi Fadhilah Syafria Fajar Delli Wihartiko Fildza Novadiwanti Firdaus, Husni Firdaus, Nova Fredicia Fredicia Galih Kurniawan Sidik Galih Kurniawan Sidik Galih Kurniawan Sidik Gendut Suprayitno Gita Adhani GUNARSO GUNARSO Hardhienata, Medria Kusuma Dewi Hastuadi Harsa Herianto Herianto Heru Sukoco Hidayat Hidayat Hidayat I Wayan Astika Ibrahim, Firmansyah Iis Rodiah Imas Sukaesih Sitanggang Indah Prasasti Indah Puji Astuti Indra Jaya Inggih Permana Irman Hermadi Irmansyah . Irsal Las Irsal Las ISKANDAR ZULKARNAEN SIREGAR Kana Saputra S Karlisa Priandana Kikin H Mutaqin Kudang Boro Seminar Laila Sari Lubis Laila Sari Lubis Lailan Syaufina Lidya Ningsih Lina Karlinasari Liyantono . Luh Kesuma Wardhani M. Cholid Mawardi M. Mukhlis Marcelita, Faldiena Medria Kusuma Dewi Hardhienata Mindara, Gema Parasti Mohamad Solahudin Muhammad Adib Zamzam Muhammad Ardiansyah Muhammad Rafi Muttaqin Mushthofa Mustakim Mustakim Mustakim Mustakim Muttaqin, Muhammad Rafi Niswati, Za'imatun Noviyanti, Inna Nurhayati, Yosi Popong Nurhayati Pratistya, Sayu Desty Puspita Kartika Sari Puspita Kartika Sari Putri Yuli Utami Raehan, Siti Raharja, Aditya Cipta Rahmat Hidayat Rangga Pebrianto Rangga Rizal Amegia Saputra Rizal Syarief Rizaldi Boer Rizki, Arviani RR. Ella Evrita Hestiandari Samsuzana Abd Aziz Santo, Deni Sanusi Sanusi Sari Agustini Hafman Savitri, Siska Sholihah, Walidatush Sidik, Galih Kurniawan Siregar, Ardinsyah Sitanggang, Imas S. Siti Kania Kushadiani Sony Hartono Wijaya Sri Dianing Asri Sri Hendrastuti, Elisabeth Sri Nurdiati Sri Wahjuni Stephane Douady Suharno Suharno Suharno Sumanto Sumanto Syeiva Nurul Desylvia Taufik Djatna Thoyyibah Tanjung Toto Haryanto Trukan Sri Bahukeling Ulfa Adzkia Uliniansyah, Mohammad Teduh Vicky Zilvan Wisnu Ananta Kusuma Wisnu Jatmiko Woro Estiningtyas Woro Estiningtyas Woro Estiningtyas Yan Mitha Djaksana Yandra Arkeman Yenni Vetrita Yoanda, Sely

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