Article Per Year (5 Year)
p-Index From 2021 - 2026
5.842
P-Index
This Author published in this journals
All Journal Pixel : Jurnal Ilmiah Komputer Grafis Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURTEKSI JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Journal of Information Systems and Informatics JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Innovation Information Technology and Application (JINITA) Journal of Applied Data Sciences Journal Automation Computer Information System (JACIS) Jurnal IPTEK Bagi Masyarakat Jurnal ABDIMAS Budi Darma Ilmu Komputer untuk Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi EXPLORE Jurnal Teknik Mesin, Industri, Elektro dan Informatika Jurnal Indonesia Sosial Sains Global Science: Journal of Information Technology and Computer Science Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Sofiansyah Fadli
STMIK Lombok
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 54 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 54 Documents
Search

 2   3   4   5   6 
CLaGAtt: A Hybrid CNN-LSTM-GRU-Attention Model for Stunting Classification Based on Anthropometric Sequences Sofiansyah Fadli; Ahmad Tantoni; Novia Arista; M. Khairul Anam; Muhammad Bambang Firdaus
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1454

Abstract

Stunting is a chronic nutritional problem that requires accurate early identification because it affects child growth, cognitive development, and long-term human capital. This study adapts the CLaGAtt model, a hybrid CNN–LSTM–GRU–Attention architecture, for stunting classification using anthropometric sequence data. Rather than proposing a new deep learning architecture, the main contribution of this study lies in adapting the existing CLaGAtt framework through an integrated preprocessing pipeline, sequence construction strategy, class balancing using SMOTE, and an evaluation protocol specifically designed for stunting prediction. The preprocessing pipeline included irrelevant-column removal, data transformation, label encoding, standard scaling, class balancing using SMOTE, and sequence generation with a time step of five and a step of one. Three train–test split scenarios were evaluated, namely 90:10, 80:20, and 70:30. Experimental results showed that the 90:10 split produced the best performance, with 91.42% accuracy, 91.50% precision, 91.50% recall, and 91.43% F1-score. The 80:20 and 70:30 scenarios achieved 87.14% and 85.71% accuracy, respectively, indicating that larger training proportions improved model generalization in the available dataset. These findings suggest that the adapted CLaGAtt framework can effectively integrate convolutional feature extraction, sequential learning, and temporal attention for stunting classification from structured anthropometric data. Future work should validate the model on external datasets and integrate regional visualization to support priority intervention mapping.
User Acceptance of E-Kinerja for Employee Additional Income Policy: A TAM Study in Central Lombok Baiq Amelia Suroyani; Sofiansyah Fadli; Baiq Yulia Fitriyani
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1725

Abstract

The increasing emphasis on professionalism, accountability, and performance management among State Civil Apparatus (ASN) has encouraged government institutions to adopt digital performance systems. This study examines user acceptance of the e-Kinerja application supporting the Employee Additional Income (TPP) policy at the Environmental Agency of Central Lombok Regency using the Technology Acceptance Model (TAM). A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0.9.9 was applied to questionnaire data from 41 ASN employees. The model included Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Using (ATU), Behavioral Intention (BI), and Actual Use (AU), with AU measured through self-reported indicators rather than system-generated logs. The measurement model demonstrated satisfactory reliability and convergent validity, although several HTMT values exceeded the recommended threshold. Structurally, PEOU significantly affected PU (β = 0.826; p < 0.001), PU affected ATU (β = 1.070; p < 0.001), ATU affected BI (β = 0.842; p < 0.001), and BI affected AU (β = 0.823; p < 0.001), whereas PEOU did not significantly affect ATU (β = −0.346; p = 0.212). The model explained 68.2% of PU, 65.3% of ATU, 70.9% of BI, and 67.7% of AU. The findings indicate that e-Kinerja acceptance is primarily driven by perceived usefulness, attitudes, and behavioral intention. Practical improvements should emphasize TPP transparency, real-time feedback, data accuracy, simplified reporting, user training, and technical assistance to support adoption.
APLIKASI PENENTUAN HARGA SONGKET MENGGUNAKAN METODE EXTREME PROGRAMMING Anti Susilawati; Khairul Imtihan; Sofiansyah Fadli
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 2 No. 3 (2022): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v2i3.710

Abstract

Sukarara Village is one of the largest traditional weaving centers in Central Lombok which has several songket motifs or ornaments that have their own motifs, so that many Lombok people and people from outside the city and abroad come to see how to make songket weaving. and sizes that are sold in the market of course with prices also vary. Such as prices (3 million, 2 million 500, 1 million and 500 hundred), as well as motifs such as motifs (keker, stork, kerit, orchid, subahanale and others), as is the case with thread types such as (silk, marcis, gold, etc.) , cotton and rayon) as well as sizes such as (2 m long – 110 cm wide, 110 cm long – 190 cm wide and 60 cm long – 2 m wide). With the problem of determining the price of songket, one alternative solution that can be used is to design a songket pricing application system that can help provide solutions to buyers. The Extreme Programming method itself is the most widely used approach to software development. So in this study it is very suitable to use Xp because the nature of the application made is done quickly through the existing stages such as: planning, designing, coding, and testing. thread.
Penerapan Metode Waterfall dalam Pengembangan Sistem Informasi Pelayanan Pendudukan Berbasis Web: Studi Kasus: Desa Muncan Yeni Rahmayati; Sofiansyah Fadli; Saikin Saikin
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 4 No. 3 (2024): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v2i3.728

Abstract

The Village Government and related officials have the task of managing and handling the interests of the local society especially in muncan village area. In the service system, residents expect services that are fast, precise and accurate. Muncan village has an area of 625,000 ha with a population of 8,035 in habitants and a geographical distance between hamlets that are very far from the place of service of villagers, to overcome this, it is necessery to design a web-based information on villages services in muncan village. It is based on the result of this study, it is expected that it could provide fast, precise and accurate services in muncan villages. The research method used is the method of data collection. The type of data used is perimer data and secondary data. Perimer data in question is interview data, while secondary data is information about village history, service information system. The data analysis method used is the PIECES method. The parts of PIECES are performance, information, economy, context and on time. The results of this study are population service information systems that have replaced several activities that were previously carried out manually so as to reduce the obstacles of data recording errors, data search, population data management, and fast and accurate information. It can also increase the effectiveness and efficiency of the functions of costs, time and labor.
Implementasi Metode TOPSIS pada Sistem Pendukung Keputusan Seleksi Pemberian Pinjaman Badan Usaha Milik Desa : Studi Kasus: Desa Batu Nampar Induk Rika Nurhaliza; Maulana Ashari; Wafiah Murniati; Sofiansyah Fadli
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 4 No. 3 (2024): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v2i3.739

Abstract

The selection process for granting loans to BUMDes Sinar Linus is done conventionally, in this case it makes researchers feel interested in designing a WEB-based Decision Support System as a decision support tool for members of BUMDes Sinar Linus. The methodology used in the development of this system is a method of collecting data in the form of observation, interviews and literature study. While the analytical method used is the TOPSIS (Technique For Others Reference by Similarity to Ideal Solution) method as a decision-making method for the design method using Extreme Programming (XP) which includes planning, design, coding and testing stages. And for the testing method using Blackbox Testing includes home display, login, loan application, applicant, criteria, alternative and TOPSIS. This research resulted in a web-based decision support system that can facilitate members of the BUMDes Sinar Linus in Batunampar Village in deciding or determining prospective recipients of loan funds at BUMDes Sinar Linus.
Implementasi Metode Network Development Life Cycle Pada Rancang Bangun Jaringan Wireless Berbasis Mikrotik Putri Nirmalsari; Hairul Fahmi; Sofiansyah Fadli
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 2 No. 3 (2023): September : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v2i3.2107

Abstract

As a non-departmental government agency (LPND) the Zainuddin Abdul Madjid Meteorological Station located at Lombok International Airport Zainuddin Abdul Madjid is one of the government agencies that is engaged in meteorology, namely conducting research related to climate change, early warning especially for the West Nusa Tenggara region. West Nusa Tenggara). In this study using data collection methods namely methods of observation, interviews, literature study and using the Network Development Life Cycle (NDLC) as a method of network design/development. There are several problems encountered within the internal scope, namely in increasing the comfort of staff using the internet network which has an unstable signal for the process of exchanging data resulting in one path of using the internet network there is one username and password that is used simultaneously by all staff and guests so that it makes the internet connection less stable. So this study aims to improve the system by resetting the existing proxy at the Zainuddin Abdul Madjid Meteorological Station office and creating usernames and passwords for staff and guests who have their own bandwidth.
CLaGAtt: A Hybrid CNN-LSTM-GRU-Attention Model for Stunting Classification Based on Anthropometric Sequences Sofiansyah Fadli; Ahmad Tantoni; Novia Arista; M. Khairul Anam; Muhammad Bambang Firdaus
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1454

Abstract

Stunting is a chronic nutritional problem that requires accurate early identification because it affects child growth, cognitive development, and long-term human capital. This study adapts the CLaGAtt model, a hybrid CNN–LSTM–GRU–Attention architecture, for stunting classification using anthropometric sequence data. Rather than proposing a new deep learning architecture, the main contribution of this study lies in adapting the existing CLaGAtt framework through an integrated preprocessing pipeline, sequence construction strategy, class balancing using SMOTE, and an evaluation protocol specifically designed for stunting prediction. The preprocessing pipeline included irrelevant-column removal, data transformation, label encoding, standard scaling, class balancing using SMOTE, and sequence generation with a time step of five and a step of one. Three train–test split scenarios were evaluated, namely 90:10, 80:20, and 70:30. Experimental results showed that the 90:10 split produced the best performance, with 91.42% accuracy, 91.50% precision, 91.50% recall, and 91.43% F1-score. The 80:20 and 70:30 scenarios achieved 87.14% and 85.71% accuracy, respectively, indicating that larger training proportions improved model generalization in the available dataset. These findings suggest that the adapted CLaGAtt framework can effectively integrate convolutional feature extraction, sequential learning, and temporal attention for stunting classification from structured anthropometric data. Future work should validate the model on external datasets and integrate regional visualization to support priority intervention mapping.
Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results Saikin Saikin; Sofiansyah Fadli; Maulana Ashari
JISA(Jurnal Informatika dan Sains) Vol 4, No 1 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i1.881

Abstract

The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.
Forecasting Blood Demand Using the Support Vektor Regression Method (Case Study: Blood Transfusion Unit-PMI Central Lombok) Yati Apriati; Wafiah Murniati; Saikin Saikin; Sofiansyah Fadli; Hairul Fahmi
JISA(Jurnal Informatika dan Sains) Vol 6, No 2 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i2.1780

Abstract

Blood is an important component produced by the human body. Blood is also a very vital part of human survival. When blood levels in the human body are less than they should be, the way to overcome this is by donating blood or blood transfusion. The health facilities that organize blood donations, provide blood and distribute blood are called Blood Transfusion Units (UTD). UTD in carrying out its duties encountered several obstacles, such as blood only having a shelf life of 35 days from donation. If it has passed the expiration date, it cannot be used anymore for blood transfusions. Meanwhile, regarding the demand for blood, the need for blood is greater than those donating. Making it difficult for UTD if the demand occurs when the existing blood stock is not sufficient. And if the stock in UTD experiences an axcess, it can cause losses because the blood is wasted due to expiration. Apart form that. The problem is that in everyday life, many people’s need for blood is reduced. Many of their families intervened directly to find available donors. They even search on social networks or social media such as WhatsApp, Facebook, Instagram and others. And this shows that many of them lack donors. To anticipate these problems. So it is necessary to carry out research on forecasting blood demand using the Support Vektor Regression method at UTD PMI Central Lombok. The aim of this research is to forecast or predict the demand for blood at UTD PMI Central Lombok in the coming period. To reduce the impact of lack or excess blood. SVR is the application of Support Vektor Machine (SVM) in the case of regression to find the best dividing line in the regression function. The advantage of the SVR model is that it can handle overfiting problems in the data. The tests used to measure the best model are Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2). The results of this research shows that the best model is Support Vektor Regression (SVR) with a polynomial kernel and based on the tuning results, the parameters used are C=10, degree=1, epsilon=1. The SVR model using a polynomial kernel produces a MAPE value of 18.7502% and RMSE value of 0.6919, which means the model has very good predictive ability. Prediction accuracy was achieved with an R2 value of 0.9936 or 99.36% and an MSE value of 0.4787, which means that the prediction of blood demand data at UTD PMI Central Lombok using SVR with a polynomial kernel function had very good prediction accuracy. With predicted result in january for blood type A it was 1654, B was 920, O was 2205 and AB was 1104
Penggunaan Metode Techique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk Evaluasi Kinerja Dosen oleh Mahasiswa (Studi Kasus: STMIK Lombok) Sofiansyah Fadli; Mohammad Taufan Asri Zaen
JISA(Jurnal Informatika dan Sains) Vol 1, No 1 (2018): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v1i1.295

Abstract

In general the lecturers performance evaluation is a routinity of a College institute for improving internal quality on an ongoing basis as well as the improvement of the status of accreditation. Performance evaluation activities specifically lecturer at STMIK Lombok funded on each period, that is the end of each semester, the lecturers assessment process performed by the students. The results of this performance assessment will be use to improve the performance of evaluation material and any lecturer who was elected as a lecturer with the best performance will be given the award. To assist in the assessment process, a system that capable to support the decision of the performance assessment of lecturers needed. The purpose of this research is to evaluate the performance of Lecturers by students at STMIK Lombok. The decision-making process aided by a computerized decision support system in expectation that subjectivity in decision-making can be minimised. Decision support systems can be used as a tool for evaluating the performance of the lecturers, so hopefully it can help the policy makers in decision-making, to get objective information about the performance of lecturers based on specified criteria.
Co-Authors Ade Riyanda Putra Agusrin Agusrin Ahmad Saprun Haris Ahmad Susan Pardiansyah Ahmad Susan Pardiyansyah Ahmad Tantoni Anam, M Khairul Anti Susilawati Arifin Hargianto Baiq Amelia Suroyani Baiq Daniatan Janiah Baiq Yulia Fitriyani Didik Sudyana Didik Sudyana Efendi, Muhamad Masjun Fadli Suandi faidlul ilahi Fatrul Rozi Fitri Rohyatul Aini G Thippanna Hairani Hairani Hairul Fahmi Hasyim Asyari Jarot Dian Susatyono Jihadul Akbar Khairul Imtihan Khurniawan Eko Saputro Lalu Mutawalli Lathifah Lathifah Latifah Lina Astika Dewi linda sari Lisa Ria Fitriani M. Hizbul Wathan Maemun Saleh Maulana Ashari Melyana Febrian Mohammad Taufan Asri Zaen Muammar Patoni Muhamad Sakinul Wadi Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Budi Saputra Muhammad Fauzi Zulkarnaen Muhammad Mustakim Novia Arista Putri Nirmalsari Ria Septiani Rika Nurhaliza Riska Indah Cahyanti Saikin Samsul Ari Putra Siti Halimatun Jannah Sri Wahyuni Putri Suhaini Suhaini Sunardi Sunardi Syaikhurrahman Tejawati, Andi Tri Putri Lestari Wafiah Muniarti Wafiah Murniati Yati Apriati Yeni Rahmayati Yuan Sa'adati