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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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Analisis Sentimen Masyarakat Indonesia Terhadap Dampak Penurunan Global Sebagai Akibat Resesi di Twitter Sutresno, Stephen Aprius
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3149

Abstract

A recession is a significant reduction in economic activity and is spread across the economy at its greatest for more than a few months, but it can also be seen in Real GDP, Real Income, Employment, Industrial Production, and Wholesale-Retail Sales. Recently, there has been a lot of public opinion regarding the recession that will occur in 2023, especially in Indonesia, on various social media such as Twitter. Based on these problems, sentiment analysis was carried out on tweets to obtain information on the positive or negative polarity of these opinions using the Naive Bayes and Support Vector Machine (SVM) methods to choose a more effective way in case studies to determine sentiment predictions. The steps are taken consist of data collection, processing data, weighting data, classification process, evaluation, validation, and results and discussion. The web scraping technique was used, and after going through the data cleaning stages, a total of 780 tweet data was obtained. The results of the classification test show that the SVM method has a greater accuracy rate with a proportion of 79.5% compared to the Naive Bayes method with a proportion of 72.5%. The SVM method's prediction results also show several 144 positive and 636 negative sentiments. Judging from the Wordcloud that was formed, it can be assumed that people are worried about their economic conditions, one of which is the unstable oil price which can trigger a recession.
Penerapan Metode SAW Dalam Peringkat Hasil Pembelajaran Siswa SMK (STM) Berdasarkan Rekapitulasi Nilai Rizka, Ade; Sari, Rahayu Mayang; Ulandari, Lavenia; Pratiwi, Daratika
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3164

Abstract

Education and technology have a close relationship in their development. The process of implementing education has results that are indicators of success in the learning process. These results are the values that are processed or calculated based on the needs of each assessment. The value processing process will provide an overview of students' capabilities in understanding and mastering subjects. At the Panca Budi Medan Vocational School (STM) school, the value processing process is carried out without using a decision support system in the process of ranking student learning outcomes. The system used only performs value processing based on the presentation of student assessment components. So that teachers need a decision-making system that can help and facilitate the process, to avoid mistakes in ranking results and can save time. In this study, the SAW method is used as a multi-attribute decision support method in the process of ranking learning outcomes so that it can assist teachers in making decisions. Processing of value recapitulation comes from the assessment components, namely daily tests, midterm, and end of the semester from aspects of assessing students' knowledge and skills for each subject. The process of calculating the ranking of learning outcomes is carried out on sample data, namely 10 alternative students and 15 subject attributes in one class for one semester. The application of the SAW method and testing on sample data resulted in student ratings, namely alternative S5 with a value of 0.994 as the highest rank and S10 with a value of 0.908 as the lowest rank. Utilization of the system and application of the SAW method can help and make it easier for teachers to process grades and rank learning outcomes. So that based on the ranking results, teachers can improve the quality of teaching and provide solutions to deficiencies in the learning process
Mining Grouping Biopharmaceutical Plants in Indonesia Using the K-Means Algorithm: Application of Data Mining in Production Lubis, Ridha Maya Faza; Huang, Jen-Peng; Sigiro, Mula; Panjaitan, Joel
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3165

Abstract

There are various types of biopharmaceutical plants or medicinal plants in Indonesia, including ginger, galangal, kencur, turmeric, lempuyang and curcuma aeruginosa whose production is widespread in various provinces in Indonesia. reached 160 million USD annually. Then the application of data mining used to classify biopharmaceutical plant production data in Indonesia from this study resulted in 2 clusters namely cluster 0 which in this cluster is a cluster with low production value of biopharmaceutical plants in Indonesia, namely the Provinces of West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep. Bangka Belitung, Kep. Riau, DKI Jakarta, DI Yogyakarta, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, Central Kalimantan, Southeast Sulawesi, Maluku, West Papua and Papua. While cluster 1 is the cluster where the production rate of biopharmaceutical plants is the highest in Indonesia, namely the Provinces of North Sumatra, West Java, Central Java, East Java and South Sulawesi. From the results that have been obtained, it is hoped that it will be useful for organizations, groups or individuals engaged in the biopharmaceutical plant sector so that they can review the existing deficiencies and can increase the production of biopharmaceutical plants in each province.
Analisis Sentimen Wisatawan terhadap Kualitas Layanan Hotel dan Resort di Lombok Menggunakan SERVQUAL dan CRISP-DM Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3199

Abstract

The era of digital transformation has sparked innovations in product and service marketing strategies in various sectors, one of which is the tourism sector. In the hospitality industry context, product marketing using website-based digital media allows consumers as hotel guests to review the products and services received. The Tripadvisor website is a digital marketing platform that provides review features for app users, especially consumers, to give ratings and reviews. This study aims to analyze the quality of hotel services using the Service Quality (SERVQUAL) framework based on the results of the classification of hotel guest sentiment data using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithm by the stages of the Cross-Industry Standard Process for Data Mining (CRISP-DM). The CRISP-DM framework consists of six stages, namely: business understanding stage, data understanding stage, data preparation stage, modeling stage, evaluation stage, and deployment stage. The SERVQUAL framework consists of several dimensions: reliability dimension; responsiveness; assurance; empathy; tangibles. The review data that will be processed is the consumer review data of The Oberoi Beach Resort Lombok; Sheraton Senggigi Beach Resort; Sudamala Resort Sengiggi; Holiday Resort Lombok; Aston Sunset Beach Resort. The results of this study show that the SVM algorithm performs better than NBC, where the accuracy value is 98.57%, the precision value is 100%, the recall value is 97.14%, and the f-measure value is 98.54%. The AUC value is 100%, and the t-Test value is 98.6%. Unlike the case with the results of SVM's algorithm performance evaluation without using the SMOTE Upgrading Operator, where the accuracy value is 95.71%, the precision value is 95.71%, the recall value is 100%, and the f-measure value is 97.81%. In addition, the AUC value is 91.1%, and the t-Test value is 95.7%.
Klasifikasi Penipuan pada Rekening Bank menggunakan Pendekatan Ensemble Learning Maghfiroh, Alfiah; Findawati, Yulian; Indahyanti, Uce
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3212

Abstract

Accounts are a collection of numbers commonly used for all transactions in the banking world, from saving, withdrawing cash, to checking account balances either directly or online using m-banking. In bank accounts and the process of opening them, there are various kinds of criminal acts committed by individuals and groups. As a bank's obligation to prevent crime so that it can provide trust to the community. In an effort to prevent criminal acts of fraud can be solved using data mining techniques, namely classification. The purpose of classification is to predict the class label of an object based on existing attributes. The classification methods used in this research are extreme gradiant boosting (XGBoost) and random forest on 22029 records. In the classification process, this study uses a percentage ratio of 90% train data and 10% test data and tuning parameters processed by randomized search cross validation. The research stages start from preprocessing to evaluation and get a train score of 99.50% and a test score of 99.59% for extreme gradiant boosting (xgboost) while random forest gets a train score of 99.46% and a test score of 99.59%. These results show that the classification results of extreme gradiant boosting (XGBoost) are better than random forest.
Penggunaan Sistem Pakar dan Algoritma Fuzzy Tsukamoto untuk Mendiagnosis Mucopolysaccharidosis Type II Iskandar, Agus
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3214

Abstract

A rare condition known as Type II mucopolysaccharidosis, often known as Hunter syndrome, is characterized by defective mucopolysaccharide metabolism brought on by a lack of lysosomal enzymes. A class of methanolic illnesses known as MPS type II are inherited in a progressive and recessive way. Organs and tissues are steadily harmed by mucopolysaccharidosis type II (MPS type II). Hence early handling is recommended. Although though it progresses very slowly, the damage brought on by this illness impairs organ function, mental growth, and physical capabilities. A system known as an expert system aims to incorporate human knowledge into computers so that they can solve issues similarly way experts do. The Tsukamoto fuzzy approach, which is highly flexible and has a tolerance for data, was employed in this investigation. It is hoped that this approach will make it easier to determine if the patient has positive MPS type II or negative MPS type II. In this investigation, a value of 0.78 was produced using the Tsukamoto fuzzy method's calculating process
Penerapan Metode Fuzzy Tsukamoto Untuk Memprediksi Kebutuhan Praproduksi Pengolahan Tempe Nugroho, Fifto; Putri Yusup, Anissa Al Fatika; Awul, Maria Fatima; Babys, Rosa Angelina
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3217

Abstract

As a staple food in Indonesia, people from various socio-economic backgrounds enjoy tempeh. Due to the popularity of tempeh and its low price, its health benefits have been widely recognized. Tempe production is still dominated by home-based businesses. Due to the ever-increasing demand, the Jakasampurna tempeh entrepreneurs, in the West Bekasi region, must balance the efficient production rate by maintaining the best quality of tempeh. The impact that occurs during pre-production related to the purchase of raw materials for making tempeh causes a lack of availability of basic ingredients for tempeh, even the difference is too large, the risk of ingredients being wasted. The research is intended to help micro business tempeh producers gain efficiency when processing tempeh, by offering management and production advice based on the valid data provided. This study uses a fuzzy inference system based on the Tsukamoto technique. In-depth conversations and direct observations at the tempe factory, provide sources of information data for calculations. This study examines the role of three (3) variables-demand (X), supply (Y), and output (Z)-in the processing and production of tempeh (Z). In a situation where the value of X (demand) and Y (supply) is uncertain, and Z (output) can go up or down, this means that the three variables give results where the uncertainty is not considerable enough. The results on the fuzzy set go up and down at stock (Y). Production (Z) consists of many and few fuzzy groups. The predicted amount of tempeh output, if it is known from the data with a demand of 1595 and an existing stock of 85, then a total of 1770 will be produced, thus the production rate becomes efficient by continuing to follow the recommended production amount, then there is little risk of accumulation of basic material storage because what is used is according to demand and the use of soy-based ingredients is always fresh.
Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3231

Abstract

Culinary is one of the economic activities that support national economic growth and represents the gastronomy of the archipelago in Indonesia. Culinary tourism activities have become famous for domestic and foreign tourists to experience the taste of a food based on the culture of each region. Makassar is one of the regions with diverse types of food and beverages and has a relationship with local socio-cultural values. Considering this, this study aims to analyze consumer sentiment toward food and services in ten restaurants in Makassar based on the recommendations of the Tripadvisor website using the Cross-Industry Standard Process for Data Mining (CRISPD-DM) and Service Quality (SERVQUAL) methods. The stages of CRISP-DM are as follows: the stage of understanding business processes; the stage of understanding data; the stage of preparing data; the modeling stage; the evaluation stage; and the deployment stage. The algorithms used as models are k-Nearest Neighbor (kNN), Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM). The results of this study show that the DT algorithm when using the SMOTE operator where the resulting accuracy value is 93.25%, precision is 88.74%, recall is 99.10%, and f-measure is 93.62%. In addition, the k-NN algorithm without using the SMOTE operator showed an accuracy value of 98.72%, a precision of 98.72%, a recall of 100%, and an f-measure of 99.36%. However, the resulting AUC value is 0.905 (90.5%). Meanwhile, when using the SMOTE operator, the SVM algorithm produces an accuracy value of 99.42%, a precision of 100%, a recall of 98.84%, and an f-measure of 99.42%. Meanwhile, the resulting AUC value is 1,000 (100%). Based on the ROC value, three algorithms can be used as models in the CRISPP-DM and SERVQUAL frameworks: the k-NN algorithm without SMOTE and the DT and SVM algorithms using the SMOTE operator
S Sistem Pakar Diagnosa Gangguan Kejiwaan Menggunakan Metode Inferensi Forward Chaining dan Certainty Factor Fauzan, Muhammad; Wulandari, Fitri; Haerani, Elin; Oktavia, Lola
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3232

Abstract

The era of artificial intelligence AI technology is now an advantage because the system does all the work according to the human brain. Expert Systemis abranch ofartificial intelligencethat adapts the mind and reasoning of an expert to solve a problem and make a decision so that it draws conclusions based on the facts. From cases of psychiatric disorders, this expert system is highly recommended to make it easier to find out what type of disorder you are suffering from to assist the public and experts in diagnosing diseases quickly and accurately. For this reason, researcherscreated an expert system for diagnosingpsychiatric disordersusing the forwardchaining inferencemethod and certainty factor. Based on the results of the implementation and analysis thathave been carried out in this study, it produces a software system, namely an expert system that has an easy-to-understand display, and can assist experts in diagnosing psychiatric disorders
Optimasi Fungsi Pembelajaran Jaringan Saraf Tiruan dalam Meningkatkan Akurasi pada Prediksi Ekspor Kopi Menurut Negara Tujuan Utama Ridho, Ihda Innar; Ariana, Anak Agung Gede Bagus; Windarto, Agus Perdana
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3240

Abstract

In the learning process carried out by Backpropagation the learning function is important in finding optimal results. This study aims to optimize the learning function of artificial neural networks in increasing the accuracy of coffee export predictions according to the main destination countries as research objects. This study applies the learning function to weights in Matlab, namely Gradient Descent with Adaptive Learning Rate (traingda), Gradient Descent with Momentum (traingdm), and Gradient Descent with Momentum and Adaptive Learning Rate (trainingdx) using several hidden layers, namely 15,30 and 45. Based on a series of trials conducted, the results of the study show that by implementing the Gradient Descent learning function with an Adaptive Learning Rate (trainingda) with a hidden layer of 30 it is capable of training neural networks with a better level of optimization, performing 143 iterations which produces a truth accuracy of 83%. When compared with the use of other learning functions that only last with an accuracy of no more than 78%. In general, it can be concluded that the optimization of the Gradient Descent learning function with Adaptive Learning Rate (trainda) can be applied to predict coffee exports according to the main destination countries, because the iterative process carried out to achieve convergence in increasing accuracy performs well

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