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All Journal JURNAL SISTEM INFORMASI BISNIS Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri Jurnal Matematika Sains dan Teknologi J-SAKTI (Jurnal Sains Komputer dan Informatika) CERIA (Cerdas Energik Responsif Inovatif Adaptif) Building of Informatics, Technology and Science Jurnal Informasi dan Teknologi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Insearch: Information System Research Journal International Journal Software Engineering and Computer Science (IJSECS) Joutica : Journal of Informatic Unisla Journal Of Human And Education (JAHE) Journal International Inspire Education Technology (JIIET) World Psychology Jurnal Sistim Informasi dan Teknologi Innovative: Journal Of Social Science Research Lughawiyah: Journal of Arabic Education and Linguistics Journal of Social Science Utilizing Technology Jurnal Informatika: Jurnal Pengembangan IT Journal of World Future Medicine, Health and Nursing Journal of Social Science Utilizing Technology Dedikasi Nusantara
Rini Nuraini, Rini
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Journal : Building of Informatics, Technology and Science

 1 
Pengembangan Sistem Klasifikasi Tipe Kepribadian Siswa Secara Psikologis dengan Algoritma Decision Tree C.45 Nuraini, Rini; Al Hakim, Rosyid Ridlo; Lisnawati, Tuti; Fariati, Wieke Tsanya
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): December 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (483.474 KB) | DOI: 10.47065/bits.v3i3.1045

Abstract

In the world of education knowing the personality type of students is very important. This is because a person's personality is influential in his learning activities and how he digests and captures the material presented by the teacher. For this reason, knowing the classification of students' personalities needs to be identified so that teachers or students themselves can optimize self-change in a better and positive direction. This study aims to develop a psychological classification system for student personality types using the C.45 decision tree algorithm. The personality type used as a class in the classification is based on psychology, including: Sanguine, Phlegmatic, Choleric and Melancholic. In this study, a web-based system was developed, so that it is easy to use for teachers and students to recognize the personality of these students. To determine the personality of students psychologically, students answer questions in the system, then the system will classify based on the answers from these students. The C.45 decision tree algorithm serves to find knowledge or patterns of characteristic similarity in a particular group or class. From the test results, the pecision value is 90%, the recall is 85% and the accuracy is 88%. This shows that the C.45 decision tree algorithm can perform personality type classification well
Implementasi Jaringan Syaraf Tiruan Menggunakan Metode Self-Organizing Map Pada Klasifikasi Citra Jenis Ikan Kakap Nuraini, Rini
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.v4i3.2558

Abstract

Snapper is one of the favorite fish for consumption because it has a myriad of benefits for the human body. There are many types of snapper, especially snapper which is often found in Indonesian waters. Knowing the types of snapper is important knowledge because snapper has different characteristics, for example there are snapper that can be consumed and there are also types of snapper that can be cultivated. However, the lack of information and similar types of snapper makes it difficult for people to identify the type of snapper. This study aims to implement a Self-Organizing Map (SOM) artificial neural network for classification of snapper species based on color and texture characteristics. In order to provide information about the snapper object to be classified, color and texture feature extraction is used. In color feature extraction, RGB and HSV parameters are used and for texture features, the Gray Level Co-occurrence Matrix (GLCM) approach is applied. Furthermore, the characteristic results obtained will be classified using the Self-Organizing Map (SOM) algorithm which divides the input patterns into certain classes so that the network output is in the form of classes that have similarities to the given input. Based on the results of the accuracy test, the built model is capable of producing an accuracy of 89.89%. Thus, the SOM model built for image classification of snapper species is in the good category.
Implementation of Profile Matching in the Decision Support System for Best Student Selection Rumandan, Rhaishudin Jafar; Nuraini, Rini; Sari, Marliana
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.v4i3.2587

Abstract

The determination of outstanding students is carried out by schools, which provide scholarships to their students in order to provide motivation to improve their academic achievement. The selection of the best students is usually done by looking at the student's report card scores, followed by the homeroom meeting. This is considered not objective and requires a long time to select the best students. Through a decision-support system, the best students can be selected based on the best criteria and alternatives. In a decision support system, a method is needed to determine the best alternative. The profile matching method is considered to have a better level of objectivity because, to measure the value of each indicator, the valuation variable is derived again with sub-indicators and is weighted using assessment parameters. This study aims to solve the problem of selecting the best student through a decision-support system with the output in the form of a ranking using the profile matching method. Based on the calculation of profile matching, it shows that the results of calculating the final score of the best student 1 are a final score of 3.000, the best student 2 gets a final score of 2.955, and the best student 3 gets a final score of 2.693. From the final score, first place in the selection of the best student was Putri Nurlandari with a final score of 3.000.
Klasifikasi Jenis Tanaman Fast Growing Species Menggunakan Algoritma Radial Basis Function Berdasarkan Citra Daun Nuraini, Rini; Harlena, Silvia; Amalya, Farida; Ariestiandy, Deny
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.v4i4.3245

Abstract

Indonesia has vast forests, even ranked as the third largest forest in the world. However, currently many forest areas have been deforested or the phenomenon of losing tree cover and forest areas. Forest rehabilitation programs develop by prioritizing plant or tree species that have fast growth or are called fast growing species. However, many people do not know about these fast growing species. Even though knowledge about the types of fast growing plant species is very important for the community to have so that the community can find out which plants can accelerate forest rehabilitation. Fast growing species of plants can actually be identified from the shape of the leaves. This study aims to build a classification model for fast growing species plant images based on leaf images by applying the Radial Basis Function (RBF) artificial neural network algorithm with morphological feature extraction. Morphological feature extraction is used to identify the shape of an object in order to obtain feature values based on predetermined parameters. These features then become input for the RBF artificial neural network to obtain learning patterns. The RBF network has three layers that are feedforward so that it can support solving classification or pattern recognition problems. Based on the results of accuracy testing, an accuracy value of 87.50% was obtained. This means that the Radial Basis Function (RBF) neural network is able to classify fast growing plant species based on leaf images.
Sistem Pendukung Keputusan Pemilihan Platform Investasi P2P Lending Menggunakan Metode Complex Proportional Assessment (COPRAS) Bagir, Muhammad; Riyanto, Umbar; Nuraini, Rini; Kustiawan, Dedi
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.v4i4.3246

Abstract

Through technological developments, many fintech P2P lending have emerged which are competing to offer convenience in transactions and offer fast processes. To determine a P2P lending platform as a place to invest, one must know in advance about the company profile or the application and programs offered as a whole. This of course will take a long time to select a P2P lending platform. If you choose an inappropriate P2P lending platform, it will result in losses. The purpose of this research is to build a Decision Support System (DSS) for choosing a P2P lending platform by implementing the Complex Proportional Assessment (COPRAS) approach in order to get the right decision and not take a long time. The COPRAS approach has the ability to produce the best alternative which is limited to alternative analysis through alternative assumptions by providing utility judgment so that the attributes of each alternative are arranged based on intervals. Based on the results of the case studies conducted, the highest utility score was Danamas Lender with a score of 100, then followed by Alami Funding Sharia with a score of 99.2338, Accelerant with a score of 89.8827 and Amartha Microfinance with a score of 83.4988. In addition, based on the results of black box testing, it shows that the software can run as it should.
Implementation of Complex Proportional Assessment and Rank Order Centroid Methods for Selecting Delivery Services Trianto, Joko; Dartono, Dartono; Nuraini, Rini; Rusdianto, Hengki
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3512

Abstract

Choosing the right delivery service partner is an important thing for companies to consider. This is because the selection of the right delivery service partner can minimize the risks involved. Generally, choosing a delivery partner service is done by looking at the profile of the freight forwarder's partner. It takes time to determine the right delivery service partner. This study aims to apply the Complex Proportional Assessment (COPRAS) and Rank Order Centroid (ROC) methods in a decision support system for selecting delivery service partners to make it easier to make the right decisions and meet needs. The ROC weighting method is used to determine the value of the criteria based on priority. Meanwhile, the COPRAS approach is used to determine the best solution based on an analysis of the existing options through alternative assessments by providing interval-based utility judgments. In the case study conducted, the best alternative was obtained, namely J&T Express with a score of 100, followed by JNE Express with a value of 92.09, SiCepat with a value of 91.89, Ninja Express with a value of 91.42. The COPRAS calculation results on the system developed with the manual calculation results show the same value, this means that the calculations on the system are valid. The usability scores, on the other hand, have an average value of 88.33% and are considered good
Classification of Character Types of Wayang Kulit Using Extreme Learning Machine Algorithm Fatmayati, Fryda; Nugraheni, Murien; Nuraini, Rini; Rossi, Farli
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3568

Abstract

Wayang Kulit, which is an original Indonesian culture, is conditioned by the meaning of life in every performance. However, Wayang Kulit is currently less popular among young people due to a lack of understanding of the art of Wayang Kulit performance. To be able to provide knowledge to the younger generation about Wayang Kulit, one of which is by introducing the characters that exist in Wayang Kulit performances. This study aims to build an image classification system for Wayang Kulit characters by applying the neural network method using Extreme Learning Machine (ELM) and morphological feature extraction. Morphological feature extraction provides information about the shape characteristics of objects present in the image which are then used for input in the classification process. The Extreme Learning Machine (ELM) method may arbitrarily establish the weight value between the input neurons and the hidden layer during the classification step, resulting in a quicker learning pattern. Based on the test results using the confusion matrix, the accuracy value is calculated to get a value of 81%.
Co-Authors ., Rusliyawati Adi Wibowo Ady Bakri, Asri Afrizal Zein Alamsyah, Dedy Alfry Aristo Jansen Sinlae Ali, Amir Almahdali, Fadila Amsal Nasution, Muhammad Ariestiandy, Deny Baharuddin Baharuddin Barroso, Uwe Cahyadi Supyansuri Damuri , Amat Daniar - Sofeny, Daniar - Dartono, Dartono Dendy K. Pramudito Desri , Syuryatman Didit Hadayanti, Didit Dito Anurogo, Dito Dwi Irnawati Edhie Rachmad, Yoesoep Fariati, Wieke Tsanya Farida Amalya Fauziah, F Fiidznillah, Rizki Fryda Fatmayati Guilin, Xie Handayani, Nurdiana Hapzi Ali Hariyono Hariyono Harlena, Silvia Herlan, Agus Ida Farida indah mukarromah Indra Jaya Indri Yani, Indri Intes, Amina Irwanto Irwanto Iskandar Fitri, Iskandar Jiao, Deng Judijanto, Loso Junaed, Ismail Khak, Muhammad Kushariyadi Kusnadi, Iwan Henri Kustiawan, Dedi Lamboy Sinaga, Victor Legito Liesnaningsih, Liesnaningsih Lisnawati, Tuti Lubis, Ahmadi Irmansyah Mahfuz, Taufik Warman Marliana Sari, Marliana Matnin MoHa, luqman Muchlis, Muhammad Muttaqin Muhamad Risal Tawil Muhammad Bagir Muhammad, Fauzan Murien Nugraheni Nurul Hikmah Pahmi Pahmi Prayogi, Bayu Setyo Purwito . Pusitasari, Mariana Diah Qoidul Khoir Ramadhan, Syam Rambe, Mirza Syadat Rijal, Syahmsu Rini Susanti Riyanto, Umbar Rossi, Farli Rosyid Ridlo Al-Hakim Rumandan, Rhaishudin Jafar Rusdianto, Hengki Sari, Linda Ratna Sarip, Mohamad Septarini, Ri Sabti Siti Nurhayati Siwi Anggraeni, Gita Soares, Teotino Gomes Syafii, Muhammad Faiz Taslim, Denis Taufik, Deni Ahmad Tonggiroh, Mursalim Tri Yusnanto TRIANA, ENDANG SHYTA Trianto, Joko Tusriyanto Tusriyanto Uky Yudatama Utami, Eva Yuniarti Victor Benny Alexsius Pardosi Wahyuddin, M Iwan Wahyul Amien Syafei Wahyul Amien Syafei Widia Nurdiani, Tanti Yudaningsih, Nunik Zuhroh, Siti