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Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 889 Documents
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Identifikasi Kualitas Kesegaran Ikan Menggunakan Algoritma K-Nearest Neighbor Berdasarkan Ekstraksi Ciri Warna Hue, Saturation, dan Value (HSV) Jerandu, Charmelia Yunizar; Batarius, Patrisius; Sinlae, Alfry Aristo Jansen
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2613

Abstract

Fish has a very high nutritional content and is needed by the human body, such as a protein. With the increasing production, and need for consumption of goodand fresh fish, irresponsible sellers take advantage of this situation by selling fish that are not fit for consumption, such as fish that are not fresh (rotten), fish that contain chlorine and formalin which can be detrimental to consumers. The purpose of this study was to determine how accurate the identification of fish freshness quality using the extraction of Hue, Saturation, and Value (HSV) color characteristics. The research method used is K-Nearest Neighbor (KNN) and is classified into several parts, namely, data collection techniques, needs analysis, design, training, and then testing. The image sample data used in this study amounted to 240 images consisting of fresh and non-fresh fish images, which will then be divided into training data and test data. The training data sample amounted to 220 images with a division of 110 fresh fish images and 110 non-fresh fish images, while the test data sample totaled 20 images with a division of 10 fresh fish images and 10 non-fresh fish images. Analysis of color features is carried out on the gills and head or the area around the eyes of the fish using Red, Green, and Blue (RGB) colors, which will be converted into Hue, Saturation, and Value (HSV) color spaces for the extraction and training processes to obtain results. The results showed that the use of HSV color character extraction was successfully applied with an accuracy value in the training of 94.09% and testing of 90%
Implementation of Neural Machine Translation for English-Sundanese Language using Long Short Term Memory (LSTM) Ramadhan, Teguh Ikhlas; Ramadhan, Nur Ghaniaviyanto; Supriatman, Agus
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2614

Abstract

In this modern era, machine translation has been used all over the world for solving humankind’s problems such as it deals with language. Machine translation is almost used by people who want to translate their native language into their foreign language. The international language being used is the English language. Machine translation is the task to translate a source language to another language. The input of it is a word or a sentence from the source language and it will be translated into another language. The input of it is a word or a sentence from the source language and it will be translated into another language. There are many purposes for using machine translation such as learning another language, communicating, finding a certain or better word to use, and even writing something in a book or another article. Several methods have been conducted to do the machine translation task such as the statistical approach and the neural approach In terms of Sundanese machine translation, there are several methods or several approaches that other researchers have conducted. However the study about Sundanese machine translation, none of the research conducted the English into Sundanese language. In this study using the encoder and decoder LSTM architecture achieve a good result regarding building a model for machine translation task. The performance of this model has achieved 0.99 accuracies in both training and testing as well as less than 0.1 loss value to both training and testing data. This model also achieves more than 0.8 average BLEU score for both training and testing data.
Penerapan Algoritma Hash Based dalam Penemuan Aturan Asosiasi Penjualan Tanaman Hias Triayudi, Agung; Sumiati, Sumiati
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2626

Abstract

Technology is very influential in the world of increasingly fierce business competition so that business people must find strategies to increase sales results in the midst of business competition. Ornamental plant sellers must be smart in managing stock and making strategies in selling ornamental plants. Transaction data can be processed into information needed to increase sales results, one of which can be used as an analysis of the rules of the buyer transaction association in purchasing ornamental plants so that it can be processed and can support decision making on ornamental plant supplies and can assist officers in recommending other ornamental plants to buyers in a cross selling strategy. Knowing the ornamental plants that are often purchased will be a top priority that must be provided so that there is no stock shortage. In this case, data mining is needed to manage sales transaction data for ornamental plants at the Sindy Flower Shop using a Hash Based algorithm. Hash Based Algorithm that can optimally determine the frequent itemset of candidate itemset. In its application in determining the rules for selling associations of ornamental plants by applying a Hash Based algorithm to obtain frequent itemsets for the 3-itemset Dahlia, Empasen and Melati which are a combination of 3-itemset ornamental plants which are prioritized in sales with a support value of 25% and confidence of 60%
Analisis Penerapan Metode Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) dan Metode Weighted Aggregated Sum Product Assessment (WASPAS) pada Pemilihan Mekanik Sepeda Motor Terbaik Sari, Venny Novita; Alinse, Rizka Tri; Sallaby, Achmad Fikri
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2627

Abstract

A company engaged in the sale and repair of motorcycles, of course, requires a reliable and responsible mechanic in his work, for that a motorcycle mechanic is needed in accordance with the terms and conditions required by the motorcycle company. Many mechanics are now opening motorbike repair services, after repairing the motorbike it becomes even worse than before it was repaired, so that it can harm consumers of the repair service. This is because a mechanic only has desperate capital without any experience and abilities that can be guaranteed. Therefore, in selecting the best mechanic, a decision support system is needed with various methods that can be used, such as the TOPSIS method, VIKOR, MOSRA, AHP, MOORA, WASPAS, WP and so on. In this study, the methods used are Multi Objective Optimization On The Basis Of Ratio Analysis (MOORA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods. Each alternative will be assessed based on the criteria for Trouble Shooting, Working Time, Education and Warning Letter, then the process of compiling or ranking each alternative is carried out, so that the best alternative or motorcycle mechanic is obtained. The results after applying the MOORA method, namely alternative A2 is the best mechanic with a value of 1.637 and the WASPAS method is the best mechanic in Q2 with alternative A2 with a value of 0.834. Both methods produce the same results, namely the best mechanic is A2
Implementasi Metode Forward Chaining dan Certainty Factor Pada Sistem Pakar Diagnosis Penyakit Sinusitis Nurerwan, Mizani Achmad; Wulandari, Irma Rofni; Astuti, Yuli; Widayani, Wiwi
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2630

Abstract

Sinusitis is similar to symptoms of minor illnesses such as runny nose, cough, and headache. Mild symptoms cause people to ignore these symptoms often. Besides, limited costs and doctor's practice hours make consultations difficult. Signs of infection that are not treated quickly can cause complications, and the infection can spread to the eye sockets or the brain. One of the ways to diagnose sinusitis early is to use an expert system so that in this study implemented the Certainty Factor and forward chaning methods to create a system that can diagnose sinusitis according to the symptoms felt and provide information about the disease and how to treat sinusitis symptoms early. Forward chaining is used as an inference method, and the certainty factor is used to calculate the level of probability of disease based on the expert's belief value and the symptoms of sinusitis selected by the user. The data used is disease data consisting of four types of sinusitis and fifteen symptoms. Based on the results of black box testing, the system that has been built functions well as expected. Expert systems in diagnosing have an accuracy of 70%.
Rekomendasi Kualitas Getah Karet Terbaik Berbasiskan Sistem Pendukung Keputusan dengan Metode MAUT Aldo, Dasril
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2631

Abstract

Rubber latex is a raw material that is widely used as the main ingredient in the manufacture of various kinds of tools, so that the quality of the rubber latex must be paid close attention. In connection with this quality, there are several things that must be considered including the age of the tree, tapping time, dry rubber content, color and texture. The problem in determining the best quality of rubber latex is that it is not paid enough attention to by farmers and most farmers tap rubber tree sap only based on feeling or hunch. If this continues to happen, it will cause the production of tools that use rubber latex to become of less quality. In addition, the production department will also need a long time to select quality raw materials. Decision support systems can be used as an alternative to help with these problems. The Multi-Attribute Utility Theory (MAUT) method is used as a method for analyzing sample data from rubber latex based on the value of each existing criterion. The stages of the MAUT method have seven processes, namely the starting stage, determining alternatives and also criteria, displaying inputted data and alternatives, determining the weights for each criterion, creating a normalization matrix and decision matrix, then doing the sum of the results of normalization with criterion weights. After obtaining the decision value, the system will display the results of recommendations or decisions from each available alternative. From testing 30 rubber latex data, it was found that 17 data showed good quality with a value of 0.56 to 1.00 and 13 data showed bad quality with a value of 0.20 to 0.55. After a comparison between manual calculations and the system built did not show any difference in results, so that the decision support system built can be a tool to assist in making decisions regarding the quality of rubber latex
Klasifikasi Nama Paket Pengadaan Menggunakan Long Short-Term Memory (LSTM) Pada Data Pengadaan Fajri, Fathorazi Nur; Syaiful, Syaiful
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2635

Abstract

Every year the government always holds procurement of goods and services (tenders) which are informed through the Electronic Procurement Service (LPSE) or the General Procurement Plan Information System (SIRUP). The process of selecting the type of procurement is still manual, namely by selecting the package category so that it is possible for mistakes to occur such as the type of service procurement into the category of goods procurement type or vice versa. Therefore, this research proposes to use the Natural Language Processing (NLP) method that can classify these packages based on existing categories. The method used is Long Short-Term Memory (LSTM) by comparing existing classification methods such as naïve bayes, logistic regression, decision tree, XG Boost, Gradient Boost, Random Forest and Support Vector Machine. The results obtained by the LSTM method have a higher accuracy than other methods, with an accuracy of 90.25%. With a parameter configuration of 100 units in the LSTM layer, epoch 10, batch size 64 and validation step 5
The Role of Digital Forensic Experts in Cybercrime Investigations in Indonesia Based on The Scopus Research Index Subektiningsih, Subektiningsih; Hariyadi, Dedy
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2638

Abstract

The development of information technology, besides having a positive impact, has negative impacts, such as an increase in cybercrime. Several parties are involved in handling cybercrimes in Indonesia, including expert witnesses in digital forensics. It is hoped that cybercrimes can be uncovered through a digital forensic approach. Research related to cybercrime and Digital Forensics in Indonesia is experiencing increasing growth from 2010 to 2021. A very rapid increase was shown in 2017. Through bibliometric analysis, you can analyze expert witnesses in the field of digital forensics by region in Indonesia, such as Sumatera, Western Java, Central Java, and Eastern Java. An expert witness in digital forensics must have technical skills in operating digital forensic tools and analysis and academic skills in uncovering crimes. The results of this study are very useful for law enforcement officers or lawyers in determining expert witnesses in the field of digital forensics. List of digital forensic experts in Indonesia who have been tested from their scientific studies by conducting various paper publications. So, law enforcement can synergize with digital forensic experts to solve cybercrime. One of the roles of a digital forensic expert is as an expert witness in court. Through research, it is hoped that there will be an increase in awareness of digital forensic experts to conduct scientific publications so that it is easier to be recognized by law enforcement and academics for updating methods and techniques of cybercrime investigation
Implementasi Algoritma Support Vector Machine Terhadap Klasifikasi Pose Balet Romindo, Romindo; Barus, Okky Putra; Pangaribuan, Jefri Junifer; Pratama, Yudhistira Adhitya; Wiliem, Evelyn
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2647

Abstract

Ballet is considered as one of the most difficult dance due to its technical posture demanded. If performed without guidance it may cause bad posture to ballerina and some serious injuries. A model in identifying different ballet poses is developed with artificial intelligence in order to tear down this barrier. The main purpose of this paper is to demonstrate a methodology that simplified Ballet Pose Recognition using an opensource framework called MediaPipe and a machine learning algorithm called Support Vector Machine. How the model work is it will pass through two stages: first, it extracts data points from an image dataset using the MediaPipe Pose Estimation library, and then it preprocesses the data, trains, validates, and tests it using the Support Vector Machine algorithm to do some pose classification. The model is trained in seven distinct ballet poses, including First Position, Second Position, Third Position, Fourth Position, Fifth Position, Tendu Devant, and Tendu Derrière. This is purposely done in order to assess the competence of the classification model. An accuracy score of 87% is achieved from the ballet pose classification model and is developed to work on images and live videos.
Pemanfaatan Machine Learning dengan Algoritma X-Means untuk Pemetaan Luas Panen, Produktivitas, dan Produksi Padi Hakim, Irma; Rafid, M.; Anggraini, Fitri
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2654

Abstract

Rice plants are essential for the world, especially Indonesia because it is a rice-producing plant that is useful as a staple food for its people. A decreased harvest area, production, and rice productivity can affect food availability. Therefore, this research aims to classify and map the harvested area, production, and productivity of rice in Indonesia based on each province. The research data used in this paper is data on the harvested area (ha), production (tons), and rice productivity (Ku/ha) by Provinces in Indonesia for 2020-2022 obtained from the Indonesian Central Bureau of Statistics website. In this study, the algorithm used is X-Means Clustering with the help of the Rapid Miner application. The results of this study are in the form of grouping or mapping of harvested area, production, and productivity of rice, divided into 3 (three) regions, including 1. Harvested Area (divided into five groups: Very high Harvested Area consists of 3 provinces, High Harvested Area consists of 1 province, Medium Harvest Area consists of 3 Provinces, Low Harvest Area consists of 8 Provinces, and Very low Harvest Area consists of 19 Provinces 2. Rice Production Area (divided into five groups: Very high rice production consists of 3 provinces, Rice production High rice production consists of 1 province, Medium rice production consists of 3 Provinces, Low rice production consists of 8 Provinces, and Very low rice production consists of 19 Provinces 3. Regions of Rice Productivity (divided into five groups: Very high rice productivity consists of 6 provinces, High Rice Productivity consists of 13 provinces, Medium Rice Productivity consists of 7 Provinces, Low Rice Productivity consists of 4 Provinces, and Very Low Rice Productivity consists of 4 Provinces. This can be information for the Indonesian government, especially for the respective provincial governments, to be able to maintain the harvested area, production, and productivity of rice in Indonesia to remain stabel.

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