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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 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 Jurnal Pustakawan Indonesia Makara Journal of Science 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,
Aerospace Engineering Agriculture, Biological Sciences & Forestry 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 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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Dampak Teknologi Informasi dan Komunikasi terhadap Produktivitas Pangan di Indonesia Pratistya, Sayu Desty; Suharno, Suharno; Buono, Agus
Agrikultura Vol 35, No 3 (2024): Desember, 2024
Publisher : Fakultas Pertanian Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/agrikultura.v35i3.58339

Abstract

Permintaan pangan dunia diperkirakan meningkat sebesar 35% - 56% antara tahun 2010-2050, peningkatan permintaan ini terkait dengan adanya perkiraan populasi dunia yang meningkat sebesar 9,8 miliar orang pada tahun 2050. Hal ini menyebabkan perlunya tindakan untuk meningkatkan produksi pangan dunia. Saat ini, inovasi teknologi yang relatif baru adalah teknologi pertanian presisi. Pertanian perisisi bertujuan untuk mengadopsi layanan atau perangkat teknologi Informasi dan komunikasi (TIK) untuk mengumpulkan dan memproses informasi. Pemanfaatan dan penerapan TIK pada bidang pertanian sudah banyak dilakukan oleh berbagai negara seperti Amerika Serikat, Cina, Jepang, dan Afrika. Penerapan teknologi informasi di negara-negara tersebut memberikan kontribusi terhadap peningkatan produktivitas hasil pertanian yang berdampak pada peningkatan pendapatan petani. Di Indonesia, ketahanan pangan selalu menjadi prioritas nasional untuk memenuhi kebutuhan pangan penduduknya yang terus bertambah, yang saat ini berjumlah 278 juta orang dengan tingkat pertumbuhan tahunan sebesar 1,07%. Penelitian ini bertujuan untuk menganalisis pengaruh teknologi informasi dan komunikasi terhadap produktivitas pangan di Indonesia. Penelitian ini menggunakan data skunder dengan memilih satu provinsi di Indonesia yaitu Jawa Timur. Pemilihan lokasi dilakukan secara multistage sampling. Hasil penelitian dengan menggunakan model Propensity Score Matching (PSM) menunjukan penggunaan internet oleh 65,13% atau 86.751 dari 133.187 rumah tangga petani berpengaruh positif terhadap produktivitas pangan yang dilihat dari sisi produktivitas lahan sebesar 1,34 persen, akan tetapi tidak signifikan secara statistik. Namun, hal tersebut tetap menunjukkan adanya potensi positif yang dapat diperoleh. Kemungkinan, pengaruh penggunaan internet terhadap produktivitas lebih kompleks dan membutuhkan penelitian lebih lanjut dengan mempertimbangkan faktor-faktor lain seperti kualitas internet, karakteristik sosial ekonomi petani, dan aksesibilitas infrastruktur.
Strategi UMKM Dalam Mendorong Masyarakat Untuk Menggunakan Qris Sebagai Alat Pembayaran Digital Di Kota Bogor Firdaus, Husni; Buono, Agus; Syarief, Rizal
MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah Vol. 20 No. 1 (2025): Manajemen IKM
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/mikm.20.1.7-15

Abstract

The development of the financial technology industry in the world of fast and secure electronic, for the beginning of digital financial transaction services in various models. The research aims to provide strategic advice and recommendations to MSMEs in the food and beverage sector to encourage people to use QRIS as a digital payment tool. The research was conducted in the city of Bogor, the location was chosen purposively based on considerations of the city of Bogor as a culinary destination. Data analysis techniques are qualitative and quantitative, another analysis process is carried out on the internal and external environment using SWOT analysis, and to support the use of quantitative analysis in the IFE matrix, EFE, IE, and QSPM. Characteristics of MSMEs include the type of marketing used by gender, age, education, income, means of payment owned, length of business, reasons for using QRIS as a means of payment, price, target customers. The results of the IE matrix analysis show that food and beverage MSME business actors in Bogor City are in cell IV position with a grow and build strategy. The results of the SWOT and QSP analysis can be concluded with the main priority being to ask for support from the government (Bank Indonesia), Payment Service System Operators (PJSP) and banking in the form of promotional media support, system improvements and fee reduction support for merchants. This support motivates and encourages MSMEs to direct and advise the public and consumers to use QRIS as their first choice for transactions.
Pengembangan model akustik dengan deep neural network untuk sistem pengenalan wicara bahasa Indonesia Gunarso, Gunarso; Buono, Agus; Mushthofa, Mushthofa; Uliniansyah, Mohammad Teduh
AITI Vol 22 No 1 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i1.84-100

Abstract

The Deep Neural Network (DNN)-based approach offers significantly higher accuracy compared to traditional methods such as Hidden Markov Model (HMM)-Gaussian Mixture Model (GMM) in acoustic model development. In this research, three popular DNN variants were evaluated: Time-Delay Neural Network (TDNN), Long Short-Term Memory (LSTM), and a hybrid combination of TDNN-LSTM for acoustic model development in Indonesian speech recognition. Using the KDW-BPPT-50K-ASR1 speech data for over 92 hours, acoustic models were trained, and experiments were conducted to analyze their performance. Research results show that the hybrid TDNN-LSTM model achieved the best performance with a Word Error Rate (WER) of 9.67%, outperforming TDNN with a WER of 12.16% and LSTM with a WER of 10.6%. This finding confirms that the hybrid model is able to improve the accuracy of Indonesian speech recognition compared to using TDNN or LSTM separately. These results provide a significant contribution to the development of more accurate and efficient speech recognition systems.
Prediksi Waktu Tanam Cabai Rawit Berdasarkan Kondisi Lingkungan Berbasis Internet of Things (IoT) Menggunakan Metode Neural Network Djaksana, Yan Mitha; Agus Buono; Sri Wahjuni; Heru Sukoco
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5199

Abstract

In Indonesian cuisine, the red Tabasco pepper holds a significant place as a commonly used ingredient. However, the cultivation of this chili variety is not without its challenges, primarily due to the volatile nature of the chili prices. Farmers often struggle with the critical decision of when to plant Tabasco peppers to optimize their yields and income. Understanding the complexities of this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advances in Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study focuses on the development of an IoT-based solution aimed at helping farmers in precisely determining the optimal planting time for Tabasco pepper. It uses five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight (hours) and groundwater usage data (m3) to make data-driven planting decisions. The urgent need for such a system becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Using historical data from 2019, obtained from the Jakarta Provincial Government Open Data DKI, and climate data from the Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The result is a reliable prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market.
Biological constraint in digital data encoding: A DNA based approach for image representation Muttaqin, Muhammad Rafi; Herdiyeni, Yeni; Buono, Agus; Priandana, Karlisa; Siregar, Iskandar Zulkarnaen; Kusuma, Wisnu Ananta
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1747

Abstract

Digital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. The goal of this research is to develop a digital data encoding technique based on DNA, while considering biological constraints such as homopolymer and GC-content. The process involves converting image pixel values into binary format, followed by encoding into DNA sequences, ensuring they meet biological constraints. The validity of the resulting DNA sequences is assessed through transcription and translation processes. Additionally, Multiple Sequence Alignment analysis is conducted to compare the similarities between the encoded DNA sequences. The results indicate that the DNA sequences from MNIST images share similar characteristics, reflected in the phylogenetic tree's close clustering. Multiple Sequence Alignment analysis shows that biological constraints successfully preserved the core visual features, allowing accurate clustering. However, this method also faces drawbacks, particularly in the reduction of visual information and sensitivity to changes in image intensity. Despite these challenges, DNA-based encoding shows potential for digital image representation. Further development, particularly the integration of deep learning, could lead to more efficient, secure, and sustainable data storage systems, especially for image data.
Hybrid convolutional vision transformer for extrusion-based 3D food-printing defect classification Mawardi, Cholid; Buono, Agus; Priandana, Karlisa; Herianto, Herianto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3311-3323

Abstract

Deep learning is generally used to perform remote monitoring of three-dimensional (3D) printing results, including extrusion-based 3D food printing. One of the widely used deep learning algorithms for defect detection in 3D printing is the convolutional neural network (CNN). However, the process requires high computational costs and a large dataset. This research proposes the Con4ViT model, a hybrid model that combines the strengths of vision transformer with the inherent feature extraction capabilities of CNN. The locally extracted features in the CNN were merged using the transformers’ global features with four transformer encoder blocks. The proposed model has a smaller number of parameters compared to other lightweight pre-trained deep learning models such as VGG16, VGG19, EfficientNetB2, InceptionV3, and ResNet50. Thus, the proposed model is simplified. Simulations were conducted to classify defect and non-defect images obtained from the printing results of a developed extrusion-based 3D food printing device. Simulation results showed that the model produced an accuracy of 95.43%, higher than the state-of-the-art techniques, i.e., VGG16, VGG19, MobileNetV2, EfficientNetB2, InceptionV3, and ResNet50, with accuracies of 77.88, 86.30, 82.95, 90.87, 84.62, and 93.83%, respectively. This research shows that the proposed Con4ViT model can be used for 3D food printing defect detection with high accuracy.
Milk Production Estimation Model for Cattle Based on Image Processing using Random Forest, XGBoost, and LightGBM Niswati, Za'imatun; Nurdiati, Sri; Buono, Agus; Sumantri, Cece
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7585

Abstract

Milk is a livestock product consumed by individuals of all ages. Therefore, it is essential to increase milk production in Indonesia to meet domestic demand. The growth of dairy cattle populations and milk production has not been able to keep up with rising consumption, resulting in a reliance on imports for most dairy products and their derivatives, with imports steadily increasing over the years. Therefore, alternative solutions are needed to enhance the milk production. One approach is to develop a milk production estimation model to determine the optimal number of dairy cattle to be cultivated by farmers and livestock companies to meet domestic demand. The objective of this study was to create a dairy milk production estimation model through image analysis using the Random Forest, XGBoost, and LightGBM algorithms. The milk production estimation model used in this study used CLAHE for contrast enhancement and VGG-16 for feature extraction. The results showed that XGBoost provided the best performance, explaining 74% of the data variation in the Y variable with a relatively small estimation error of 0.92. After parameter tuning using Grid Search, an improvement was observed, where XGBoost explained 86% of the data variation in the Y variable, and the estimation error decreased to 0.72. Image processing and machine learning technologies are part of precision agriculture that aims to improve the efficiency, productivity, and sustainability of livestock operations.
Co-Authors Ade Fruandta Adi Rakhman Aditya Cipta Raharja Agung Prajuhana Putra 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 Rahmad Benyamin Kusumoputro Bib Paruhum Silalahi Budi Nugroho Cece Sumantri Dhany Nugraha Ramdhany Dian Kartika Utami Djaksana, Yan Mitha Edi Santosa Ekowati Handharyani Elisabeth Sri Hendrastuti Endang Purnama Giri Erliza Hambali Erliza Noor Ernan Rustiadi Fadhilah Syafria Fadhilah Syafria Fajar Delli Wihartiko Fildza Novadiwanti Firdaus, Husni Firmansyah Ibrahim Fredicia Fredicia Galih Kurniawan Sidik Galih Kurniawan Sidik Galih Kurniawan Sidik Gema Parasti Mindara Gendut Suprayitno Gita Adhani GUNARSO GUNARSO Hastuadi Harsa Herianto Herianto Hidayat Hidayat Hidayat I Wayan Astika Ibrahim, Firmansyah Iis Rodiah Imas Sukaesih Sitanggang Indah Prasasti Indah Puji Astuti Indah Puji Astuti Indra Jaya Inggih Permana Inna Noviyanti 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 Liyantono . M. Cholid Mawardi M. Mukhlis Marcelita, Faldiena Medria Kusuma Dewi Hardhienata Mohamad Solahudin Muhammad Ardiansyah Muhammad Rafi Muttaqin Mushthofa Mustakim Mustakim Mustakim Mustakim Muttaqin, Muhammad Rafi Niswati, Za'imatun Nova Firdaus Nurhayati, Yosi Popong Nurhayati Pratistya, Sayu Desty Puspita Kartika Sari Puspita Kartika Sari Putri Yuli Utami Raharja, Aditya Cipta Rahmat Hidayat Rizal Syarief Rizaldi Boer Rizki, Arviani RR. Ella Evrita Hestiandari Santo, Deni Sanusi Sanusi Sari Agustini Hafman Savitri, Siska Sholihah, Walidatush Sidik, Galih Kurniawan Siregar, Ardinsyah Sitanggang, Imas S. Siti Kania Kushadiani Siti Raehan Sony Hartono Wijaya Sri Dianing Asri Sri Nurdiati Sri Wahjuni Stephane Douady Suharno Suharno Suharno Sumanto, Sumanto Syeiva Nurul Desylvia Taufik Djatna Thoyyibah Tanjung Toto Haryanto Trukan Sri Bahukeling Uliniansyah, Mohammad Teduh Vicky Zilvan Wisnu Ananta Kusuma Wisnu Jatmiko Woro Estiningtyas Woro Estiningtyas Woro Estiningtyas Yandra Arkeman Yenni Vetrita Yoanda, Sely