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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pertanian Tanaman Pangan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal TAM (Technology Acceptance Model) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Indonesian Journal of Electrical Engineering and Computer Science
Sitanggang, Imas S.
Bogor Agricultural University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science

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Poisson Clustering Process on Hotspot in Peatland Area using Kulldorff’s Scan Statistics Method Annisa Puspa Kirana; Imas Sukaesih Sitanggang; Lailan Syaufina
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.2272

Abstract

The increase in peatland fire’s intensity has encouraged people to develop methods of preventing wildfire. One of the prevention methods is recognizing the distribution pattern of hotspot as one of forest and land fire indicators. We could determine the area that has high fires density based on distribution patterns so any early prevention steps could be performed in that area. This research proposed to recognize the distribution pattern of hotspot clusters in the peatland areas in Sumatera in the year 2014 using Kulldorff’s Scan Statistics (KSS) method with Poisson model. This approach was specifically designed to detect clusters and assess their significance via Monte Carlo replication. Results showed that the method is reliable to detect the clusters of hotspots which have the accuracy of 95%. Riau and South Sumatera province have the highest density of cluster distributions of the hotspot. Based on the maturity level of peat, cluster distributions of hotspot were mostly found in ‘hemic’ maturity level. Based on peatland thickness, cluster distribution of hotspot was mostly found in ‘very deep’ thickness.
PEMETAAN DAERAH RAWAN KEBAKARAN DI LAHAN GAMBUT BERDASARKAN POLA SEKUENS TITIK PANAS DI KABUPATEN PULANG PISAU KALIMANTAN TENGAH Anissa Rezainy; Lailan Syaufina; Imas Sukaesih Sitanggang
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol. 10 No. 1 (2020): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan
Publisher : Graduate School Bogor Agricultural University (SPs IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.10.1.66-76

Abstract

Land and forest fire is one of the major that caused Indonesia’s deforestation, who has a significant impact to the environment, loss of conservation, air pollution and economic loss. This research makes a spatial modelling along with factor that can affect collerates the forest fire. Spatial model of vulnerability of land and forest fire is built by composite mapping analysis method. Hotspot that is used in this research is the results of data mining processing, with sequential pattern mining technique which to find the relationships between the occurances of sequential event and pattern that often appear. From the six variables that influence land and forest fire there are four variables that impacts on the study area, that is forest zone, depth of peatland, distance of irrigation, and distance of road. The fire in the area of study occurs many times in the peatland area with the depth of 400-800 cm. Land and forest fire occurs frequently in 100-900 meters from irrigation and land and forest fire also occurs frequently in 1-4 km form the road. Land and forest fire occurs frequently in protected forest
Modul Front-End Sistem Informasi Geospasial Patroli Terpadu Kebakaran Hutan dan Lahan Deny Ramdhany; Imas Sukaesih Sitanggang; Ikhsan Kurniawan; Wulandari Wulandari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (732.191 KB) | DOI: 10.29207/resti.v5i2.3045

Abstract

To prevent and handle forest and land and forest fire (karhutla), the Ministry of Environment and Forestry assembled a patrol team that conducts a daily task to observe directly to the hotspot location as an indication for land fire. Currently, the patrol team reported the investigation result into a group chat. This method consumed many storage spaces and not suitable for formal reporting. This study aims to develop a front-end module for a web GIS application that visualizes the patrol team's daily report. The application has its data recapitulation method and able to create a formal report. The data used in this study are a set of the report that collected in 2016 by Sumatera and Kalimantan patrol team. The steps to build this application include communication, integrate with the API from the back-end system, developing functional needs, software testing, and the last is software release. The application was build using HTML and CSS for its interface and Javascript and API from the back-end module for its content management. The system uses Google Maps services and library to support the functionalities of the application. The unit testing method's test result shows that the module runs well and can afford all of the required functionality. In addition, the system testing result that the ratio between actual error and expected error is equal to 1. This result indicates the functions of the system are working properly according to the use cases of the system.
Purchase Recommendation and Product Inventory Management using Content Based Filtering with Sequential Pattern Mining Approach Aditya Cipta Raharja; Imas Sukaesih Sitanggang; Agus Buono
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 4, November 2018
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (493.795 KB) | DOI: 10.22219/kinetik.v3i4.663

Abstract

Today, the product sales at XYZ Bookstore are increase in accordance to the trend in society. In that case, high sales must be supported by good supply and on target. Product sold based on needs of consumers will make possibility to achieve high sales. Using the Sequential Pattern Mining approach, we can specify sales patterns of products in relation to another products. SPADE (Sequential Pattern Discovery using Equivalence classes) is an algorithm that can be used to find sequential patterns in a large database. This algorithm finds frequent sequences of the sales transaction data using database vertical and join process of the sequence. The results of SPADE algorithm is frequent sequences which are used to form the rules. Those can be used as predictors of other items that will be purchased by consumers in the future. The result of this study is a lot of unique sequence appears that can provide the best advice for Merchandiser Officer, for example, there are 1.468 sequences that prove the customer who bought the product in Children’s Book category will always bought the same thing in the others day. This research produce some recommendation, one of the recommendation is Children's Book category has a very high chance of being a Best Seller for a long time so that the purchasing officer on XYZ bookstore should ensure that the product's supply of the category is always safe throughout the year. It means SPADE is successfully used to provide the advice and Merchandiser Officer must ensure the stock of that product is always available to avoid Lost Sales.
TEKNIK PENGUJIAN BOUNDARY VALUE ANALYSIS PADA APLIKASI LEARNING MANAGEMENT SYSTEM POLINELA Zuriati Zuriati; Dewi Kania Widyawati; Imas Sukaesih Sitanggang; Agus Buowo
Jurnal TAM (Technology Acceptance Model) Vol 9, No 2 (2018): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (347.928 KB)

Abstract

This study discusses the testing process of the Lampung State Polytechnic e-learning application called the Polinela Learning Management System (LMS). The testing phase is one of the stages in the development cycle of a software. This stage is a series of activities carried out to find errors or shortcomings of a software or computer application. There are several techniques used to test software including black box testing techniques. In this study using black box testing with a boundary value analysis technique to test e-learning applications that have been applied to the Informatics Management program of the Lampung State Polytechnic. The boundary value analysis technique works by determining the lower and upper limit values of the data to be tested, then the data is inputted into the LMS Polinela application. The stages of research carried out begin by determining the functionality to be tested, designing test scenarios, determining the data to be tested, determining the upper and lower limit values according to the database structure that has been created, conducting testing experiments, documenting the results of research, and drawing conclusions. The test results show that there were no errors in the LMS Polinela application when validating the data to be processed.
Downscaling Modeling Using Support Vector Regression for Rainfall Prediction Sanusi Sanusi; Agus Buono; Imas S Sitanggang; Akhmad Faqih
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i8.pp6423-6430

Abstract

Statistical downscaling is an effort to link global scale to local scale variable. It uses GCM model which usually used as a prime instrument in learning system of various climate. The purpose of this study is as a SD model by using SVR in order to predict the rainfall in dry season; a case study at Indramayu. Through the model of SD, SVR is created with linear kernel and RBF kernel. The results showed that the GCM models can be used to predict rainfall in the dry season. The best SVR model is obtained at Cikedung rain station in a linear kernel function with correlation 0.744 and RMSE 23.937, while the minimum prediction result is gained at Cidempet rain station with correlation 0.401 and RMSE 36.964. This accuracy is still not high, the selection of parameter values for each kernel function need to be done with other optimization techniques.
Density Based Clustering of Hotspots in Peatland with Road and River as Physical Obstacles Prima Trie Wijaya; Imas Sukaesih Sitanggang; Lailan Syaufina
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i3.pp714-720

Abstract

Indonesia has the largest peatland area among tropical countries, covering about 21 milions ha, which spread mainly in Sumatera, Kalimantan, and Papua. Land and forest fires occur almost every year in peatland areas in Indonesia. One of indicators for forest and land fires is hotspot. The objective of this study is to group hotspots with road and river as obstacles using the CPO-WCC (Clustering in Presence of Obstacles with Computed number of Cells) algorithm. Clusters of hotspot data were analyzed based on peatland area distribution. This study also evaluates the results of clustering on peatlands in order to obtain the best clusters. Clustering using CPO-WCC algorithm produces three clusters of hotspot. The area of dense cluster is 10202.10 km2 with number of hotspots per km2 is 0.985. The higest number of hotspots occurrence is found in peatland with type of Hemists /Saprists (60/40) and depth greater than 400 cm.
Face recognition based on Siamese convolutional neural network using Kivy framework Yazid Aufar; Imas Sukaesih Sitanggang
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp764-772

Abstract

Human face recognition is a vital biometric sign that has remained owing to its many levels of applications in society. This study is complex for free faces globally because human faces may vary significantly due to lighting, emotion, and facial stance. This study developed a mobile application for face recognition and implemented one of the convolutional neural network (CNN) architectures, namely the Siamese CNN for face recognition. Siamese CNN can learn the similarity between two object representations. Siamese CNN is one of the most common techniques for one-shot learning tasks. Our participation in this study determined the efficiency of the Siamese CNN architecture with the enormous quantity of face data employed. The findings demonstrated that the suggested strategy is both practical and accurate. The method with augmentation produces the best results with a total data set of 9000 face images, a buffer size of 10000, and epochs of 5, producing the minimum loss of 0.002, recall of 0.996, the precision of 0.999, and F1-score of 0.672. The proposed method gets the best accuracy of 98% with test data. The Siamese CNN model is successfully implemented in Python, and a user interface and executables are built using the Kivy framework.
Estimation of biomass of forage sorghum (sorghum bicolor) Cv. Samurai-2 using support vector regression Kahfi Heryandi Suradiradja; Imas Sukaesih Sitanggang; Luki Abdullah; Irman Hermadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1786-1794

Abstract

One alternative to improve feed quality is to combine the main feed with forages which are more economical in cost but contain high protein sources, such as sorghum. Production estimation is essential because it will determine the sustainability of the feed. This study aimed to estimate the amount of sorghum production using support vector regression (SVR). Several stages of this research are collecting data, preprocessing, modelling, and evaluation. The dataset used and the input for this SVR algorithm model is field observation data. The kernels used in the SVR algorithm modelling are linear, Polynomial, and RBF. Sorghum production estimation using SVR has a performance evaluation value that refers to the root mean square error (RMSE). The result of this research is that the model obtained from the SVR algorithm can estimate sorghum production with performance evaluation values using R2, mean absolute error (MAE), mean absolute percentage error (MAPE), and RMSE. The best results on the Polynomial kernel are R2=0.7841, MAE=0.0681, MAPE=0.46641, and RMSE=0.1006. This study shows that the classification model obtained from the SVR algorithm with Kernel Polynomial is the best model for estimating sorghum production.
SKYLINE QUERY BASED ON USER PREFERENCES IN CELLULAR ENVIRONMENTS Ruhul Amin; Taufik Djatna; Annisa Annisa; Imas Sukaesih Sitanggang
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4192

Abstract

The recommendation system is an important tool for providing personalized suggestions to users about products or services. However, previous research on individual recommendation systems using skyline queries has not considered the dynamic personal preferences of users. Therefore, this study aims to develop an individual recommendation model based on the current individual preferences and user location in a mobile environment. We propose an RFM (Recency, Frequency, Monetary) score-based algorithm to predict the current individual preferences of users. This research utilizes the skyline query method to recommend local cuisine that aligns with the individual preferences of users. The attributes used in selecting suitable local cuisine include individual preferences, price, and distance between the user and the local cuisine seller. The proposed algorithm has been implemented in the JALITA mobile-based Indonesian local cuisine recommendation system. The results effectively recommend local cuisine that matches the dynamic individual preferences and location of users. Based on the implementation results, individual recommendations are provided to mobile users anytime and anywhere they are located. In this study, three skyline objects are generated: soto betawi (C5), Mie Aceh Daging Goreng (C4), and Gado-gado betawi (C3), which are recommended local cuisine based on the current individual preferences (U1) and user location (L1). The implementation results are exemplified for one user located at (U1L1), providing recommendations for soto betawi (C5) with an individual preference score of 0.96, Mie Aceh Daging Goreng (C4) with an individual preference score of 0.93, and Gado-gado betawi (C3) with an individual preference score of 0.98. Thus, this research contributes to the field of individual recommendation systems by considering the dynamic user location and preferences.
Co-Authors Aditya Cipta Raharja Agus Buono Agus Buowo Ainuddin A. N Ainuddin A. N. Akhmad Faqih Alusyanti Primawati Andi Nurkholis Anissa Rezainy Annisa Annisa Annisa Puspa Kirana Baba Barus Deden Sumirat Hidayat Deny Ramdhany DEWI APRI ASTUTI Dewi Kania Widyawati Fitri Insani GONO SEMIADI Heru Sukoco Ikhsan Kurniawan Irman Hermadi Kahfi Heryandi Suradiradja Khaira, Ulfa Koentjoro, Bambang S. Lailan Syaufina Luki Abdullah Makarim, Abdul Karim Marimin , Norwati Mustapha Norwati Mustapha Prima Trie Wijaya Putri Thariqa Razali Yaakob Razali Yaakob Ruhul Amin Sanusi Sanusi Sobir Sobir Suci Sri Utami Sutjipto Taufik Djatna Wulandari Wulandari Yazid Aufar