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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Komputer dan Agri-Informatika Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Informasi Sains dan Teknologi (ISAINTEK)
Muhammad Asyhar Agmalaro
Department Of Computer Science, IPB University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Other

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Identifikasi Tanaman Buah Tropika Berdasarkan Tekstur Permukaan Daun Menggunakan Jaringan Syaraf Tiruan Agmalaro, Muhammad Asyhar; Kustiyo, Aziz; Akbar, Auriza Rahmad
Jurnal Ilmu Komputer dan Agri-Informatika Vol 2, No 2 (2013)
Publisher : Departemen Ilmu Komputer IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (683.921 KB)

Abstract

Indonesia merupakan salah satu negara dengan keanekaragaman tanaman buah tropika yang cukup tinggi. Keanekaragaman tanaman buah tropika tersebut merupakan satu tantangan dalam melakukan identifikasi. Identifikasi tanaman dapat dilakukan berdasarkan buah, bunga, maupun daun. Identifikasi berdasarkan daun merupakan identifikasi yang lebih mudah dilakukan karena daun akan ada sepanjang masa, sedangkan bunga dan buah mungkin hanya ada pada waktu tertentu. Identifikasi tanaman menggunakan daun dapat dilakukan berdasarkan bentuk, tekstur, maupun warna citra daun tersebut. Pada penelitian ini, ekstraksi fitur gray level co-occurrence matrix (GLCM) dari tekstur citra permukaan daun buah tropika digunakan sebagai input dari pelatihan Jaringan syaraf tiruan untuk proses identifikasi. Secara keseluruhan, pengujian dengan menggunakan hidden neuron sebanyak 7 menghasilkan hasil akurasi terbaik, yaitu 90%.Kata kunci: buah tropika, daun, GLCM, jaringan syaraf tiruan, tekstur.
Model Spasial untuk Prediksi Konsentrasi Polutan Kabut Asap Kebakaran Lahan Gambut Menggunakan Support Vector Regression Muhammad Asyhar Agmalaro; Imas Sukaesih Sitanggang; Lailan Sahrina Hasibuan; Muhammad Murtadha Ramadhan
Jurnal Ilmu Komputer & Agri-Informatika Vol. 5 No. 2 (2018)
Publisher : Departemen Ilmu Komputer - IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (498.701 KB) | DOI: 10.29244/jika.5.2.119-127

Abstract

Kabut asap dari kebakaran lahan gambut mengandung berbagai macam polutan seperti CO dan CO2. Polutan tersebut dapat berimplikasi buruk pada kesehatan masyarakat sekitar peristiwa itu terjadi yang berupa Infeksi Saluran Pernafasan Atas (ISPA). Penelitian ini bertujuan untuk membuat model spasial untuk prediksi konsentrasi polutan kabut asap yang berupa CO dan CO2 dari kebakaran lahan gambut di Sumatra tahun 2015. Model spasial dibentuk menggunakan algoritme support vector regression (SVR) dengan kernel radial basis function (RBF) dengan melihat konsentrasi polutan dari beberapa titik tetangga. Parameter tuning dilakukan untuk mendapatkan nilai parameter paling optimal dari SVR. Hasil penelitian menunjukkan bahwa model spasial prediksi konsentrasi CO terbaik didapatkan pada gamma dengan nilai 20 yang menghasilkan root mean squared error (RMSE) dan nilai koefisien korelasi sebesar 1,174242×10-8 dan 0,5879287. Model spasial prediksi konsentrasi CO2 terbaik dibentuk pada gamma dengan nilai 10 yang menghasilkan RMSE dan nilai koefisien korelasi sebesar 9,843717×10-8 dan 0,6058418. Hasil prediksi dari model yang dibentuk telah dapat mengikuti pola nilai aktual konsentrasi polutan. Kata Kunci: CO, CO2, kabut asap, model spasial, support vector regression.
Identifikasi Tanaman Buah Tropika Berdasarkan Tekstur Permukaan Daun Menggunakan Jaringan Syaraf Tiruan Muhammad Asyhar Agmalaro; Aziz Kustiyo; Auriza Rahmad Akbar
Jurnal Ilmu Komputer & Agri-Informatika Vol. 2 No. 2 (2013)
Publisher : Departemen Ilmu Komputer - IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (683.921 KB) | DOI: 10.29244/jika.2.2.73-82

Abstract

Indonesia merupakan salah satu negara dengan keanekaragaman tanaman buah tropika yang cukup tinggi. Keanekaragaman tanaman buah tropika tersebut merupakan satu tantangan dalam melakukan identifikasi. Identifikasi tanaman dapat dilakukan berdasarkan buah, bunga, maupun daun. Identifikasi berdasarkan daun merupakan identifikasi yang lebih mudah dilakukan karena daun akan ada sepanjang masa, sedangkan bunga dan buah mungkin hanya ada pada waktu tertentu. Identifikasi tanaman menggunakan daun dapat dilakukan berdasarkan bentuk, tekstur, maupun warna citra daun tersebut. Pada penelitian ini, ekstraksi fitur gray level co-occurrence matrix (GLCM) dari tekstur citra permukaan daun buah tropika digunakan sebagai input dari pelatihan Jaringan syaraf tiruan untuk proses identifikasi. Secara keseluruhan, pengujian dengan menggunakan hidden neuron sebanyak 7 menghasilkan hasil akurasi terbaik, yaitu 90%. Kata kunci: buah tropika, daun, GLCM, jaringan syaraf tiruan, tekstur.
Penerapan Logika Fuzzy pada Pemantauan Kualitas Proses Produksi Muhammad Asyhar Agmalaro
KOMPUTASI Vol 7, No 1 (2010): Vol. 7, No. 1, Juli 2010
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (607.164 KB) | DOI: 10.33751/komputasi.v7i1.1776

Abstract

Pemantauan kualitas suatu proses produksi dalam dunia industri telah lama dilakukan menggunakan pendekatan stastical process control. Melalui pendekatan statistical processcontrol, Pemantauan proses produksi dilakukan dengan menggunakan suatu alat bantu yang dinamakan dengan control chart. Control chart akan menghasilkan keputusan untuk memberhentikan proses produksi apabila terdapat identifikasi yang menunjukan tanda-tanda penyimpangan atauout-of- control saat proses produksi sedang berlangsung. Keputusan pemberhentian tersebut terkadang menimmbulkan ketidakjelasan yang menyebabkan tidak jarang proses produksi menjadi kurang maksimal. Oleh karena itu, sangat diperlukan suatu pendekatan lain seperti teori fuzzy untuk mencoba memecahkan masalah tersebut.(Kata Kunci: fuzzy, statiscal process control, control chart out-of-control).
Sentinel-1A image classification for identification of garlic plants using decision tree and convolutional neural network Risa Intan Komaraasih; Imas Sukaesih Sitanggang; Annisa Annisa; Muhammad Asyhar Agmalaro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The Indonesian government launched a garlic self-sufficiency program by 2033 to reduce imports by monitoring garlic lands in several central garlic areas. Remote sensing using satellite imageries can assist the monitoring program by mapping the garlic lands. A previous study has classified Sentinel-1A satellite imageries to identify garlic lands in Sembalun Lombok Indonesia using the decision tree C5.0 algorithm with three scenarios data input and produced a model with an accuracy of 78.45% using scenarios with two attributes vertical-vertical (VV) and vertical-horizontal (VH) bands. Therefore, this study aims to improve the accuracy of the classification model from the previous study. This study applied two classification algorithms, decision tree C5.0 and convolutional neural network (CNN), with two new scenarios which used two new combinations of attributes). The results show that the use of new data scenarios as input for C5.0 can not increase the previous model's accuracy. While the use of the CNN algorithm shows that it can improve the previous study's accuracy by 7.91% because it produced a model with an accuracy of 86.36%. This study is expected to help garlic land identification in the Sembalun area to support government programs in monitoring garlic lands.
Pemodelan Berbasis Jaringan untuk Pengklasifikasian Kanker Payudara Berdasarkan Data Molekuler Mushthofa; Chamdan L Abdulbaaqiy; Sony Hartono Wijaya; Muhammad Asyhar Agmalaro; Lailan Sahrina Hasibuan
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.101-113

Abstract

Cancer is a disease characterized by uncontrolled cell growth. One of the characteristics of uncontrolled growth is the presence of estrogen-receptor-positive (ER+). About 67% of breast cancer test results have ER+. Breast cancer profiles are divided into 4 subtypes, namely: Luminal A, Luminal B, basal-like, and HER-2 enriched. Each category has a different effect on adjuvant chemotherapy. In this study, a network-based approach was used to select features/molecular biomarkers that have the potential to assist modeling and classifying sub-types of breast cancer. The molecular features used are Copy Number Alteration (CNA) and gene expression. The feature selection results were compared with the PAM50 feature-based accuracy from the literature study. The results indicate that the features selected from this network-based approach can obtain a comparable performance w.r.t the original PAM50 features, and can be used as alternative to perform breast cancer subtyping.
Pengembangan Modul Otomatisasi Pengunduhan Citra Sentinel-1A Berbasis Web Menggunakan Metode Prototyping Muhammad Asyhar Agmalaro; Imas Sukaesih Sitanggang; Taufik Hidayat
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 2 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.2.137-148

Abstract

Sentinel-1A imagery can be used for various purposes, such as surveys and agricultural land use mapping. For example, Sentinel-1A image can be used to carry out land processing and validate crop yields from horticultural crops such as garlic. However, the acquisition and download of Sentinel images are currently done manually with several stages, so it still needs to be more effective and efficient. Therefore, an alternative way to support the acquisition of sentinel data is necessary by optimizing the process of automating the download of Sentinel data. This study aims to build a front-end module to automate the downloading of web-based Sentinel image data using the Django Framework. The prototyping method is used to develop a front-end module for Sentinel image download automation. This method was chosen based on its advantages in getting feedback from each user from every iteration carried out so that improvements can be made quickly according to user needs. The result of this research is an automated system for downloading Sentinel-1A images that can download Sentinel image data via maps or by validating geoJson data entered by the user. The development of this system is carried out in two iterations. All functions in the developed module were successfully performed in black box testing without showing any errors.
Pengenalan Strategi Digital Marketing untuk Usaha Mikro Kecil dan Menengah di Kelurahan Situ Gede, Kecamatan Bogor Barat, Kota Bogor Muhammad Asyhar Agmalaro; Dea Amanda
Agrokreatif: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 9 No. 2 (2023): Agrokreatif Jurnal Ilmiah Pengabdian Kepada Masyarakat
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/agrokreatif.9.2.258-268

Abstract

The development of digital technology, and internet has brought considerable changes to marketing activities. The marketing trends have been shifted from conventional to digital marketing. A proper digital marketing can reach target customers effectively and efficiently. The purpose of this activity is to help and enrich knowledge MSEM and provide an introduction to maximize the effectivet strategy to use digital technology in marketing MSME products in Situ Gede Village, West Bogor Sub-district, Bogor City. The introduction of digital marketing strategies for 15 selected MSMEs was carried out in the form of workshops and mentoring. This community service activity was started with the presentation of important points on digital marketing, followed by motivation with success stories of digital marketing by MSMEs, identification of the current technology utilization and digital marketing activities that have been carried out by participants, identification of obstacles and challenges encountered, and steps that need to be taken to overcome them. The active and enthusiastic participation of the participants in the discussion and mentoring group activities show that the knowledge and motivation of participants in adopsing digital marketinghave been succeed.
Implementasi Pendekatan Algoritma Deep Learning CNN untuk Identifikasi Citra Pasien Keratitis Agmalaro, Muhammad Asyhar; Kusuma, Wisnu Ananta; Rif’ati, Lutfah; Pramita Andarwati; Anton Suryatama; Rosy Aldina; Hera Dwi Novita; Ovi Sofia
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.10.2.164-175

Abstract

The incidence of keratitis globally ranges from 0.4 to 5.2 per 10,000 people annually. Keratitis can only be identified by an ophthalmologist using a slitlamp as a fundamental instrument for specific eye examination in secondary care facilities. In primary care facilities, eye specialists and slitlamps are not available. This causes delay in the diagnosis and treatment of keratitis patients in public health centers or areas with limited facilities and access to doctors/ophthalmologists. This research aims to develop a keratitis identification model using the convolutional neural network (CNN) method and training data consisting of images produced by smartphones and combined with slitlamp images. The training accuracy of the developed model is 92% with a dropout layer set at 0.3, and the average validation accuracy is 83%, indicating that the model training did not experience overfitting. The testing results with new data achieved an accuracy of 90%. Next, the parameters of the best model will be integrated into an application running on the Android operating system. However, the application’s functionality and UX/UI performance need to be improved to facilitate seamless use of the model.
The Development of A Mobile-Based Area Recommendation System Using Grid-Based Area Skyline Query and Google Maps Annisa; Alyssa, T. Sandra; Agmalaro, Muhammad Asyhar
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.66155

Abstract

Choosing a location for a business or place of residence is an essential task in our daily lives. Typically, a location is considered favorable if it is in proximity to profitable facilities that enhance its value while being distant from facilities that diminish its value. However, conducting surveys in the field to identify desirable candidate locations is not always feasible. Factors such as high costs, inclement weather, and transportation limitations can hinder survey activities. This study aims to develop a mobile-based system for location selection using the Grid-based Area Skyline (GASKY) algorithm in conjunction with Google Maps. Google Maps is widely utilized for location-based decisions and is familiar to mobile application users. GASKY is employed for its capability to recommend locations based on user-provided information regarding desired facilities near the target location and facilities to be avoided, eliminating the need to input candidate locations from survey results. The outcomes of this study include a mobile-based application that utilizes the Google Maps API to create data collection modules. Mobile-based applications utilizing GASKY offer convenience, as they can be accessed by users anytime and anywhere.
Co-Authors Alyssa, T. Sandra Annisa Annisa Annisa Anton Suryatama Auriza Rahmad Akbar Auzi Asfarian Aziz Kustiyo Chamdan L Abdulbaaqiy Dea Amanda Endang Purnama Giri Firman Ardiansyah Hasibuan, Lailan Sahrina Hera Dwi Novita Ibrahim, Arhammirza Imas Sukaesih Sitanggang Muhammad Murtadha Ramadhan Ovi Sofia Parastu, Rakheda Andria Pramita Andarwati Pratama, Dharma Rabbani, Rafif Rif’ati, Lutfah Risa Intan Komaraasih Rosy Aldina RR. Ella Evrita Hestiandari Sony Hartono Wijaya Taufik Hidayat Wisnu Ananta Kusuma