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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Applied Engineering and Technological Science (JAETS)

Sarjon Defit

UPI YPTK Padang

Author-ID : 2488472

Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Published : 4 Documents Claim Missing Document

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Articles

Identifikasi Anggota dalam Penempatan pada Struktur Organisasi menggunakan Metode Profile Matching Ahmadi Ahmadi; Sarjon Defit; Jufriadif Na’am
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 2 (2018): Agustus 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The organization of a political party is one organization that must have an organizational structure. Each cadre who sits in the structure must have skills that match his field. The goal is for the organization to grow better. For each cadre to occupy the appropriate structure, identification must be performed. The method used to identify is Profile Matching on the data of each prospective member. Based on the test results obtained cadre with a special aspect of 60% and the general aspect of 40% is the right one. Then this method is suiTabel to be used in identifying cadres who will occupy positions in organizational structure.

Prediksi Hasil Ujian Kompetensi Mahasiswa Program Profesi Dokter (UKMPPD) dengan Pendekatan ANFIS Fajri Marindra Siregar; Gunadi Widi Nurcahyo; Sarjon Defit
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 2 (2018): Agustus 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The main objective of this study was to predict the outcome of student's competency exam of the medical profession (UKMPPD) using Adaptive Neuro-Fuzzy Inference System (ANFIS). Data obtained from the Faculty of Medicine Universitas Riau’s student database in 2015 which amounted to 170 data. Input variables were membership status, length of study, and grade point average. Furthermore, the data were analyzed using MATLAB software by setting the number of membership function 2 2 2 and Gbell membership function. The results showed that the method is able to predict the outcome of UKMPPD with Mean Average Percentage Error (MAPE) 0.07%, minimum 0.00%, and maximum 0.44%.

Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (TF-IDF) Sintia Sintia; Sarjon Defit; Gunadi Widi Nurcahyo
Journal of Applied Engineering and Technological Science (JAETS) Vol. 2 No. 2 (2021): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

In the SiPaGa application, the codefication search process is still inaccurate, so OPD often make mistakes in choosing goods codes. So we need Cosine Similarity and TF-IDF methods that can improve the accuracy of the search. Cosine Similarity is a method for calculating similarity by using keywords from the code of goods. Term Frequency and Inverse Document (TFIDF) is a way to give weight to a one-word relationship (term). The purpose of this research is to improve the accuracy of the search for goods codification. Codification of goods processed in this study were 14,417 data sourced from the Goods and Price Planning Information System (SiPaGa) application database. The search keywords were processed using the Cosine Similarity method to see the similarities and using TF-IDF to calculate the weighting. This research produces the calculation of cosine similarity and TF-IDF weighting and is expected to be applied to the SiPaGa application so that the search process on the SiPaGa application is more accurate than before. By using the cosine sismilarity algorithm and TF-IDF, it is hoped that it can improve the accuracy of the search for product codification. So that OPD can choose the product code as desired

SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP Susandri Susandri; Sarjon Defit; Muhammad Tajuddin
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.v9i1.4201

The use of WhatsApp Group (WAG) for communication is increasing nowadays. WAG communication data can be analyzed from various perspectives. However, this data is imported in the form of unstructured text files. The aim of this research is to explore the potential use of the SentiwordNet lexicon for labeling the positive, negative, or neutral sentiment of WAG data from "Alumni94" and training and testing it with machine learning text classification models. The training and testing were conducted on six models, namely Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network. The labeling results indicate that neutral sentiment is the majority with 7588 samples, followed by 324 negative and 1617 positive samples. Among all the models, Random Forest showed better precision and recall, i.e., 83% and 64%. On the other hand, Decision Tree had slightly lower precision and recall, i.e., 80% and 66%, but exhibited a better f-measure of 71%. The accuracy evaluation results of the Random Forest and Decision Tree models showed significant performance compared to others, achieving an accuracy of 89% in classifying new messages. This research demonstrates the potential use of the SentiwordNet lexicon and machine learning in sentiment analysis of WAG data using the Random Forest and Decision Tree models

Co-Authors Ahmadi Ahmadi Fajri Marindra Siregar Gunadi Widi Nurcahyo Jufriadif Na`am, Jufriadif MUHAMMAD TAJUDDIN Sintia Sintia Susandri, Susandri

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