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All Journal TEKNIK INFORMATIKA Jurnal Ilmu Komputer dan Agri-Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Sony Hartono Wijaya
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Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering

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Analisis Potensi Lokasi dan Klasifikasi Electronic Data Capture (EDC) pada UMKM BNI Agen46 Putra, Fiqhri Mulianda; Marimin; Sony Hartono Wijaya; Nusantara, Reinaldy Jalu
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 10 No. 2 (2023)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.10.2.133-146

Abstract

Dalam era digitalisasi, peran agen-agen bank menjadi semakin penting dalam memberikan layanan keuangan kepada masyarakat. Bank BNI sebagai salah satu bank terkemuka di Indonesia, memiliki jaringan agen yang luas untuk mendekatkan layanan perbankan kepada nasabah. Dalam upaya mengoptimalkan jaringan agennya, Bank BNI melakukan analisis spasial menggunakan metode clustering K-means untuk menentukan lokasi potensial pendirian Agen46 baru di DKI Jakarta. Selain itu, juga dilakukan pembuatan model klasifikasi random forest Agen46 produktif dan non-produktif untuk mengoptimalkan penggunaan mesin EDC dan menghemat biaya operasional. Berdasarkan analisis spasial dengan metode clustering K-means, ditemukan tujuh lokasi potensial untuk pendirian Agen46 baru di DKI Jakarta, yaitu kecamatan Jagakarsa, Makasar, Pesanggrahan, Grogol Petamburan, Taman Sari, Tambora, dan Johar Baru. Model klasifikasi yang dibuat berhasil membedakan Agen46 yang produktif dan non-produktif dengan akurasi yang tinggi. Selain itu, pembuatan model klasifikasi Agen46 menjadi penting dalam mengenali agen-agen yang tidak produktif, sehingga dapat dilakukan antisipasi dan penanggulangan yang cepat untuk memperbaiki efisiensi penggunaan mesin EDC. Hasil analisis prediksi dan model klasifikasi ini diharapkan dapat memberikan panduan dan dasar kebijakan yang lebih baik bagi Bank BNI dalam menentukan lokasi penempatan mesin EDC Agen46 di masa depan. Dengan demikian, diharapkan Bank BNI dapat mempercepat proses pengklasifikasian Agen46, meningkatkan pemanfaatan mesin EDC, dan mengoptimalkan efisiensi biaya terkait dengan agen-agen BNI.
Sistem Pemantauan dan Pengendalian Logistik Buah Mangga Berbasiskan Machine Learning Hardyansyah, Buyung; Heru Sukoco; Sony Hartono Wijaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5226

Abstract

Fruits are highly perishable goods, which means they have a short shelf life and can pose significant challenges in trade. A long supply chain can trigger the process of fruit spoilage. The logistics environment, both internal and external, can also affect the decrease in quality of goods. One common issue facing producers is the variability in consumer demand for fruit quality. To address this problem, a machine learning-based logistics monitoring and recommendation system can be developed, utilizing the Long Short-Term Memory (LSTM) and Decision Tree algorithms. Using machine learning algorithms, the system can analyze data from devices equipped with the Internet of Things (IoT), such as temperature and humidity sensors, to identify potential issues in the supply chain and provide recommendations to optimize logistics operations. In this study, a machine learning-based monitoring system is developed to monitor the shelf life of perishable goods, with a specific focus on mango fruit. The system utilizes LSTM to predict mango ripeness and decision tree algorithms to recommend fruit ripeness. The objective is to provide producers with recommendations that optimize the logistics process for high-quality mangoes and meet the consumer demands for quality fruit. The implementation of a machine learning-based logistics monitoring and recommendation system can provide significant benefits to mango producers. Using advanced technologies, such as LSTM and Decision Tree algorithms, producers can optimize their logistics operations, improve fruit quality, reduce waste, and improve customer satisfaction.
Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method Rustandi, Dede; Sony Hartono Wijaya; Mushthofa; Ratih Damayanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5370

Abstract

It is important to note that some species of bamboo are protected and considered endangered. However, distinguishing between traded and protected bamboo species or differentiating between bamboo species for various purposes remains a challenge. This requires specialized skills to identify the type of bamboo, and currently, the process can only be carried out in the forest for bamboo that is still in clump form by experienced researchers or officers. However, a study has been conducted to develop an easier and faster method of identifying bamboo species. The study aims to create an automatic identification system for bamboo stems based on their anatomical structure (ASINABU). The bamboo identification algorithm was developed using macroscopic images of cross-sectioned bamboo stems and the research method used was the convolutional neural network (CNN). CNN was designed to identify bamboo species with images taken using a cellphone camera equipped with a lens. The final product is an Android automatic identification application that can detect bamboo species with an accuracy of 99.9%.
Geographical Indication Classification Based on Stingless Bee Honey Samples Using a Generative Model on Spectral Data for Mobile Device Applications Maulana, Hata; Purwanto, Yohanes Aris; Diding Suhandy; Sony Hartono Wijaya; Heru Sukoco
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.49280

Abstract

Stingless bee honey (SBH) is a food product with a simple and fast production process. Honey production is highly dependent on the condition and ability of the bees. Each region in Indonesia has different botanical and environmental conditions, with different entomological characteristics of bee species based on their geographical origin. This honey classification model is part of the formation of Geographical Indications (GI) based on product characteristics. A generative model is used in the pre-processing stage to produce spectrum data with the best grouping (silhouette score > 0.6). The main process discussed in this article is the application of GI classification model of four types of stingless bee honey based on cultivation location. The results of the study with a 400x745 dataset and 4 classes (Lampung, Bogor, Sukabumi, and Rangkas Bitung) showed that the classification model produced an accuracy above 95% with a precision and recall above 0.99. The SBH GI classification application has been successfully built using the scrum methodology and is cloud-based. The application displays the classification results of origin, type (entomological), feed dominance (botanical) and dominant spectrum values. The application has also been tested for feasibility based on the User Acceptance Test with results above 90%.
Co-Authors Diding Suhandy Hardyansyah, Buyung Hata Maulana Marimin , Nusantara, Reinaldy Jalu Putra, Fiqhri Mulianda Ratih Damayanti RR. Ella Evrita Hestiandari Rustandi, Dede