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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal MIPA Jurnal Teknik Informatika Jurnal TIMES JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan ILKOM Jurnal Ilmiah Jurnal Ilmiah Behongang Journal of Information Systems and Informatics Journal of Applied Data Sciences Operations Research: International Conference Series Journal of Agriculture (JoA) Jurnal Ilmiah Sains Bulletin of Social Informatics Theory and Application d'Cartesian: Jurnal Matematika dan Aplikasi Jurnal Informatika dan Rekayasa Perangkat Lunak
Luther Latumakulita
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

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APPLICATION OF THE FUZZY TOPSIS METHOD FOR LECTURER CERTIFICATION ASSESSMENT Raintung, Stephanie Marceline; Latumakulita, Luther A.; Paat, Franky; Karim, Irwan; Sentinuwo, Steven; Islam, Noorul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1747-1764

Abstract

Lecturer Certification (Serdos) is the method of granting educational certificates to lecturers as a formal verification of the speaker's recognition as an expert at a higher level of teaching. In Lecturer Certification, there is an Assessment of Lecturers' Self-Statements in Higher Education Tridharma Performance (PDD-UKTPT), which is divided into three Assessment Elements, namely Teaching, Research and Publication of Scientific Work and Community Service (PkM). The study focuses on teaching assessment. Sam Ratulangi University is one of the Universities Organizing Educator Certification for Lecturers (PTPS) in 2023. The Lecturer Certification assessment at Sam Ratulangi University does not describe the specific assessment range or include the importance weight of each criterion. Thus, this research aims to apply the Fuzzy TOPSIS method as an alternative in the assessment, which determines the importance and weight of each criterion and provides a description of the specific assessment range for each criterion to overcome uncertainty in the evaluation to provide clear guidelines for Serdos assessors in conducting the assessment. The research results regarding lecturer suitability decisions in assessing the Teaching Element. Therefore, it is found that Fuzzy TOPSIS can be used as an assessment method in Lecturer Certification, and it is better suited to handle the uncertainty issues often encountered in lecturer certification assessments. The result of this study provides an excellent accuracy of 100% compared with the manual method.
IMPLEMENTATION AND COMPARISON IN USING STATE PATTERN ON MAIN CHARACTER MOVEMENT (CASE STUDY : POCONG JUMP VIDEO GAME VERSION 1.0) Sintaro, Sanriomi; Salaky, Deiby Tineke; Latumakulita, Luther Alexander; Takaendengan, Mahardika Inra; Bernard, Bernard; Surahman, Ade; Islam, Noorul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0955-0968

Abstract

Game development success is often hard to achieve due to various problems such as performance issues, malfunctioning features, and poorly organized program structure. The problems that arise can be prevented by using the design pattern as a game programming architecture from the beginning of development. By implementing a design pattern, the process of developing video games can be made easier and simplified. The development team can focus its efforts on producing better quality video games. In this study, design patterns that would be used are state pattern and finite state machine. The state pattern is implemented by encapsulating the character's behavior in a class called state. Finite state machine will then facilitate the transition of states caused by user/player input or variable value changes. State pattern and finite state machine is tested with test case and game performance is tested with software metric. The result obtained from this study are state pattern and finite state machine have a valid component structure and could improve performance efficiency in video games.
Website Development of Information System Study Program UNSRAT as A Media Information Pagewang, Yalon Bu'tu; Pinontoan, Benny; Lapihu, Dodisutarma; Latumakulita, Luther Alexander; Takaendengan, Mahardika Inra; Ngangi, Stephano Caesar Wenston; Montolalu, Chriestie Ellyane Juliet Clara
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2025): Volume 6 Number 2 June 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Although the Information Systems Study Program at UNSRAT had an existing website, it failed to effectively deliver up-to-date and comprehensive academic information. Both students and the broader community often experienced difficulties in accessing essential program-related content. In addition, the site's visual design was deemed unattractive and did not meet user expectations. objective of this research is to design and implement a more interactive and informative website to enhance the efficiency of academic information dissemination. This study employs the Rapid Application Development (RAD) methodology, encompassing requirement analysis, system design through UML diagrams and wireframes, system implementation using the CodeIgniter framework, and functionality testing via the black-box testing approach. The findings revealed that all implemented website features operated as intended and aligned with user requirements. Furthermore, analysis of feedback from 70 respondents yielded an average rating of 4.1 out of 5.0, indicating that the website successfully met user expectations regarding accessibility, visual design, content relevance, and overall technical performance.
Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network Latumakulita, Luther Alexander; Arya Astawa, I Nyoman Gede; Mairi, Vitrail Gloria; Purnama, Fajar; Wibawa, Aji Prasetya; Jabari, Nida; Islam, Noorul
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1082

Abstract

Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification.  Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.
Decision Tree C4.5 Performance Improvement using Synthetic Minority Oversampling Technique (SMOTE) and K-Nearest Neighbor for Debtor Eligibility Evaluation Priyanto, Edi; Sela, Enny Itje; Latumakulita, Luther Alexander; Islam, Noourul
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1676.373-381

Abstract

Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset. Another challenge is developing an optimal model for evaluating debtor eligibility. Based on these challenges, this study aims to develop an optimal model for evaluating debtor eligibility on the German Credit Dataset, using the decision trees, k-Nearest Neighbor (k-NN) and Synthetic Minority Oversampling Technique (SMOTE). SMOTE and k-NN is used to overcome challenges regarding imbalanced datasets. While the decision tree are applied to produce a debtor classification model. In general, the steps taken are preparing datasets, pre-processing data, dividing datasets, oversampling with SMOTE, and classification models using decision trees, and testing. Model performance evaluation is represented by accuracy values obtained from the confusion matrix and area under curve (AUC) values generated by the Receiver Operating Characteristic (ROC). Based on the tests that have been carried out, the best accuracy value in the test is obtained at 73.00% and the AUC value is 0.708, in parameters k = 3 and Max-Depth = 25. Based on the analysis produced, the proposed model can improve performance compared to if the dataset is not applied SMOTE.
Identification of sea urchins in melonguane coastal area using Multilayer Perceptron Neural Network Pinilas, Andar Alwein; Latumakulita, Luther Alexander; Hatidja, Djoni
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1208.169-177

Abstract

Sea urchins (Echinoidea) are marine biota that is found in Indonesian waters and there are 950 types of sea urchins scattered throughout the world. This study aims to classify types of sea urchins based on the characteristics contained in sea urchin images using the Multilayer Perceptron Neural Network (MLP-NN) method with 3 classification classes. 120 sea urchin image data were taken from the Melonguane beach area, Talaud Islands Regency, North Sulawesi Province. In the MLP-NN stage, training, validation, and testing processes are carried out by applying 8-fold cross-validation, and the system performance shows the lowest accuracy of 93.33%, the highest 100%, and an average of 98.33%. The experimental results indicate that MLP-NN can classify sea urchins with good performance.
Mapping the Global Landscape of Electronic Supply Chain Management (e-SCM): A Bibliometric and Visual Analysis Aditya Lapu Kalua; Mochamad Agung Wibowo; Luther Alexander Latumakulita
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1525

Abstract

This study maps the intellectual structure of global electronic supply chain management (e-SCM) research through a bibliometric analysis of Scopus-indexed publications published between 2015 and 2025. The retrieval workflow began with the Scopus query TITLE-ABS-KEY ("supply chain management") and was followed by structured interface-based refinement using pub- lication period, subject area, and document type constraints to construct the final analytical corpus. Bibliometric performance indicators were analyzed using the Bibliometrix R- package, while science mapping and network visualization were conducted using VOSviewer. The findings show that the e-SCM literature is organized around five major thematic concen- trations: sustainability in supply chain management, environmental and circular-economy integration, operational decision support and risk analytics, sectoral and stakeholder coordi- nation, and the recent acceleration of blockchain, Industry 4.0, and digital transformation. Co-authorship and country-level mappings indicate a multicentric global research structure led by China, India, and the United Kingdom, while temporal overlay visualization shows a marked shift toward digitally enabled governance and resilience-oriented research during 2022–2023. These results provide an evidence-based synthesis of the evolution of the field and a replicable bibliometric foundation for future sector-specific studies in sustainability- sensitive supply networks.
Pattern Recognition of Puta Dino Fabric Using Web-Based Convolutional Neural Network Method Luther Alexander Latumakulita; Silviani Esther Rumagit; Hence Beedwel Lumentut; Frangky Jessy Paat; Jaidun Ramadhan Kaplale; Enny Itje Sela
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1103

Abstract

This study aims to develop an intelligent system capable of recognizing traditional woven motifs of Puta Dino, a culturally significant textile from Tidore Island. These motifs are visually complex, poorly documented, and hard for the public to distinguish, highlighting the need for a digital tool to support cultural preservation and accurate identification. This research is the first to build a structured Puta Dino motif database and provide an integrated model designed for real-world use. The approach captured primary images of eight validated motifs and applied systematic preprocessing, including normalization and data augmentation, to enhance variability and strengthen the dataset. A lightweight deep learning model predicated on a convolutional neural network was designed to achieve a compromise between accuracy and computational efficiency. The system was evaluated through cross-validation and independent test data, as well as multiple real-world trials utilizing a web interface. These trials involved different image capture scenarios, including from a distance, moderate distance, close and angled views, and when the fabric surface was folded. The model architecture and system interface with the system are illustrated in the relevant figures, and the tables provide performance data on the system’s training, accuracy in motif classification, and achieved results in real-world conditions. The system demonstrated excellent classification accuracy in controlled test conditions. It showed real-world competency, accurately classifying most motifs in various conditions. The data also point to specific issues with motif recognition in extreme distortion cases, which reflect the typical issues of laboratory-to-field model deployment. The outcomes clearly demonstrate both the possibilities and the limitations of the currently available recognition of culturally significant textiles. The study concludes by exploring the possibilities of expanding the dataset and increasing the depth of learning through more sophisticated techniques, as well as enhancing accessibility to promote sustained community and cultural engagement.
Co-Authors Aditya Lapu Kalua Aji Prasetya Wibawa Altien Rindengan Altien Rindengan Alwin Melkie Sambul Ambarita, Yolanda Margareta Anastasia, Lenshy Aprisilia Arista Mandagi Arthur G. Pinaria Assa, Jan Rudolf Benny Pinontoan Bernard Bernard, Bernard Bobby Polii Budiman, Glenn Chriestie E. J. C. Montolalu Chriestie E. J. C. Montolalu Dedie Tooy Deiby Tineke Salaki Djoni Hatidja Edi Priyanto Eliasta Ketaren, Eliasta Enny Itje Sela Fajar Purnama Felliks Tampinongkol, Felliks Frangky J. Paat Frangky Jessy Paat Frangky Jessy Paat Gybert Saselah Hence Beedwel Lumentut I Nyoman Gede Arya Astawa Islam, Noorul Islam, Noourul Jabari, Nida Jaidun Ramadhan Kaplale Jantje Pongoh Jevenston Lalenoh John Socrates Kekenusa Julana Rarung Julana Rarung, Julana Jullia Titaley Karim, Irwan Koibur, Mayko Edison Kusuma, Samuel D. A. Lapihu, Dodisutarma Lindsay Mokosuli Liwu, Suzanne L. Mairi, Vitrail Gloria Mamuaja, Christine F Manarisip, Endrue Jehezkiel Mandagi, Franklin Mans Mananohas Mans Mananohas, Mans Marni Sumarno Marni Sumarno, Marni Max R Kumaseh Miske Silangen Mochamad Agung Wibowo Montolalu, Chriestie Ellyane Juliet Clara NELSON NAINGGOLAN NELSON NAINGGOLAN Ngangi, Stefano C.W. Ngangi, Stephano Caesar Wenston Noorul Islam Noviania, Reski Oessoe, Yoakhim Y.E. Paat, Frangky J Paat, Franky Pagewang, Yalon Bu'tu Pinatik, Herry F Pinilas, Andar Alwein Pioh, Diane Raintung, Stephanie Marceline Rindengan, Altien J. Rinny Mamarimbing Rumambi, David P Salaky, Deiby Tineke Sandra Pakasi Sandy Laurentius Lumintang Sanriomi Sintaro Saroyo Saroyo Selvie Tumbelaka Silviani Esther Rumagit Sirait, Hasanuddin Sofia Wantasen Steven Ray Sentinuwo Sulu, Brian Sumual, Gery Josua Surahman, Ade Takaendengan, Mahardika Inra Tangkeallo, Sindy C. T. Teltje Koapaha Tenda, Edwin Tineke M. Langi Winsy Weku Winsy Weku Yohanes Langi Yohanes Langi