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All Journal CommIT (Communication & Information Technology) TEKNOINFO JTERA (Jurnal Teknologi Rekayasa) Indonesian Journal of Artificial Intelligence and Data Mining FIRM: Journal of Management Studies International Journal of Advances in Data and Information Systems Jurnal Sosial dan Teknologi IT For Society : Journal of Information Technology Jurnal Manajemen Sistem Informasi
Rusdianto Roestam
Badan Pengkajian dan Penerapan Teknologi (BPPT)
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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Analisis Dan Perancangan Sistem Informasi Promosi Berbasis Web Pada CV. Golden Property Setiawan, Roby; Roestam, Rusdianto
Jurnal Manajemen Sistem Informasi Vol 2 No 4 (2017): Jurnal Manajemen Sistem Informasi
Publisher : LPPM Universitas Dinamika Bangsa

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Abstract

Promotion is activities in marketing to offer the product to customers or consumers prospect toinforming, reminding and persuading people to accept the products, concepts and ideas. Promotion hastwo ways, there is electronic and non-electronic. At the CV. Golden Property companies,promotionalactivitiesdoing by non-electronic with brochures and banners so the promotion information to customersor consumers not prospectly. By designing a web-based information, users and managers expect cansimplify the information system as a solution to the problem doesn’t prospectly. The method used in thedesignis promotional mix where a variables can be combined with the companies needs and abilities.This web-based information would be better if next researcher insert value of financial transactions sothat will getting any benefit.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI GEOGRAFIS LOKASI APOTEK KOTA JAMBI Hernando, Irham; Roestam, Rusdianto
Jurnal Manajemen Sistem Informasi Vol 2 No 4 (2017): Jurnal Manajemen Sistem Informasi
Publisher : LPPM Universitas Dinamika Bangsa

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Abstract

Sistem Informasi Geografis (SIG) merupakan suatu sistem infromasi yang digunakan untuk menyusun, menyimpan,merevisi dan menganalisis data dan atribut yang bereferensi kepada lokasi atau posisi obyek-obyek di bumi.Salahsatu pemanfaatan sistem informasi geografis dibidang kesehatan yaitu dapat mencari lokasi apotek.Di Jambi terdiriatas 11 kecamatan dimana ada 159 lokasi apotek yang terletak di daerah 11 kecamatan tersebut.Dengan keberadaanapotek masyarakat dapat memperoleh atau membeli obat.Tetapi informasi keberadaan apotek kurang diketahuimasyarakat.Masyarakat sulit mencari lokasi apotek terdekat saat mencari obat yang diresepkan oleh dokter danmasyarakat juga sulit mencari lokasi apotek 24 jam. Adapun tujuan penelitian ini menganalisis dan merancang sisteminformasi geografis apotek kota Jambi. Permodelan sistem pada penelitian ini menggunakan alat bantu UML (UnifiedModelling Languange) seperti use case, activity diagram, dan class diagram. Sedangkan perancangan sistempenelitian ini menggunakan Eclipse sedangkan untuk menjalankan sistemnya menggunakan android. Penelitian inimenghasilkan sebuah sistem informasi geografis lokasi apotek kota Jambi yang kemudian sistem akan digunakanuntuk masyarakat untuk mencari lokasi apotek.
ANALISIS DAN PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN PENERIMA BEASISWA BANTUAN SISWA MISKIN (BSM) DENGAN METODE PROFILE MATCHING PADA SMK NEGERI 1 MUARO JAMBI Afrina, Afrina; Roestam, Rusdianto
Jurnal Manajemen Sistem Informasi Vol 2 No 3 (2017): Jurnal Manajemen Sistem Informasi
Publisher : LPPM Universitas Dinamika Bangsa

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Abstract

Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan yang dapatmemudahkan dalam proses penerima beasiswa bantuan siswa miskin. Sistem pendukung keputusan inimenggunakan metode perhitungan metode profile matching. Sistem ini menampilkan hasil perangkingansiswa yang memenuhi kriteria dalam penerimanan beasiswa. Kriteria yang digunakan dalam penelitian iniadalah aspek akademik terdiri dari nilai semester dan kelas, aspek ekonomi keluarga, terdiri dari pekerjaanorang tua, penghasilan orang tua, jumlah tanggungan orang tua dan status anak. Aspek penunjang adalahorganisasi dan prestasi non akademik. Sistem pendukung keputusan penerima beasiswa bantuan siswa miskinmembantu mempermudah dalam pengambilan keputusan.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI ADMINISTRASI AKREDITASI BERBASIS WEB PADA BADAN AKREDITASI PROVINSI PENDIDIKAN ANAK USIA DINI DAN PENDIDIKAN NON FORMAL (BAP PAUD DAN PNF) Suwarto, Suwarto; Roestam, Rusdianto
Jurnal Manajemen Sistem Informasi Vol 2 No 3 (2017): Jurnal Manajemen Sistem Informasi
Publisher : LPPM Universitas Dinamika Bangsa

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Abstract

Since acreditation program for Early Childhood Education Institution and Nonformal Education Institution wasexecuted manually. The processess require much staffs to accept accreditation proposals, check them using manualinstruments as well as reporting the checking result and reporting the riil condition of the institution. These set ofactivities are sometimes troubled by “self-tired condition” and the possibility of “data lost”. If these occured willcause unobjectivity of accreditation process even miss scoring. Due to the purpose of the research, to solve suchproblems, the writer has analyzed current system. The research object focuses on the accreditation proposing currentsystem, the scope as well as system development life cycle. The purpose of this research is to analyze problemshappened at current system as well as designing web base information system for accreditation administration. Theresult of the research is hoped to be able to solve current problems so that accreditation proposals can be processedby online system
Analisis Prediksi Level Obesitas Menggunakan Perbandingan Algoritma Machine Learning dan Deep Learning Setiyani, Lila; Indahsari, Ayu Nur; Roestam, Rusdianto
JTERA (Jurnal Teknologi Rekayasa) Vol 8, No 1: June 2023
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v8.i1.2022.139-146

Abstract

Level obesitas dapat di identifikasi berdasarkan kebiasaan makan dan kondisi fisik yang terdiri dari beberapa parameter yang dapat digunakan untuk mengukur tingkat akurasi dari prediksi level obesitas. Penelitian ini bertujuan untuk membuat prediksi level obesitas dengan akurasi yang baik. Metode yang digunakan adalah dengan membanding algoritma machine learning dan deep learning, dataset diambil dari data level obesitas pada individu dari negara Mexico, Peru dan Kolombia yang didasarkan pada kebiasan makan dan kondisi fisik, data tersebut terdiri dari 17 atribut dan 2111 record. Berdasarkan hasil analisis algoritma machine learning didapati akurasi dari random forest sebesar 96,37%, decision tree classifier sebesar 88,33%, logistic regression sebesar 73,66%, naïve bayes sebesar 56% dan KNN sebesar 88,96%. Sedangkan deep learning didapati akurasi sebesar 86,05%. Algoritma machine learning random forest memiliki akurasi yang paling baik dan dapat memprediksi presentase akurasi serta level obesitas.
Machine Learning Algorithms for Prediction of Boiler Steam Production Lianzhai, Duan; Roestam, Rusdianto; Sen, Tjong Wan; Fahmi, Hasanul; ChungKiat, Ong; Hariyanto, Dian Tri
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1339

Abstract

The continuous increase in global electricity demand has resulted in boiler power plants becoming a significant energy source. The production of steam is a principal indicator of boiler efficiency, and the accurate prediction of steam production is paramount importance for the enhancement of boiler efficiency and the reduction of operational costs. In this study employs a boiler dataset with a steam production capacity of 420 tons per hour. A total of 25 independent variables were extracted from the original 39 variables through data processing and feature engineering for the purpose of prediction analysis. Subsequently, 8 machine learning models were used for modeling predictions. Grid search cross-validation was employed in order to optimise the performance of the model. The models were analysed and assessed using the Mean Squared Error (MSE) metrics. The results show that random forest achieves the highest accuracy among the 8 single models. Based on 8 models, New Bagging ensemble model is proposed, which combined predictions from 8 single models, demonstrated the optimal overall fit and the lowest MSE, achieved the purpose of the research. The present study demonstrates the ability to analyse and predict complex industrial systems with machine learning algorithms, and provides insights into the use of machine learning algorithms for industrial big data analytics and Industry 4.0. Further work could explore using larger datasets and deep learning to make predictions more accurate.
SENTIMENT ANALYSIS OF STUDENT SATISFACTION TOWARDS DISTANCE LEARNING USING MACHINE LEARNING METHOD Andres, M; WanSen, Tjong; Roestam, Rusdianto
IT for Society Vol 9, No 1 (2024): Vol 9, No 1
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/itfs.v9i1.5073

Abstract

The Covid-19 pandemic forces the entire societyto change their way of life. One of them is the process of face-to-face learning changing into distant learning. Various responsesarise from students during the implementation of this newsystem, both positive and negative, indicating the level of studentsatisfaction. The sentiment analysis of students' commentsduring distance learning was conducted using machine learningalgorithms and tools Rapid miner. Literature study shows thatthe Naive Bayes, K-NN, and Decision Tree algorithms have veryhigh accuracy, so this research uses those methods to get high-accuracy results. The research shows the following results;Naive Bayes is 93.80% and class precision for pred. Positive93.80% and pred. negative 100.00%. The K-NN algorithm is92.49% and class precision for pred. positive is 92.37%, pred.negative 100%. The Decision Tree method is 90.81% with astandard deviation of (+-) 0.58 and class precision for pred.positive 90.81% and class pred. negative 0.00%.
Tomato Pest and Disease Identification Based on Improved Deep Residual Network and Transfer Learning Linli, Peng; Sen, Tjong Wan; Fahmi, Hasanul; Roestam, Rusdianto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.34038

Abstract

Tomatoes are a vital global crop, but their yield can be severely impacted by various diseases like leaf mold and spotted wilt. Early and accurate diagnosis of these diseases is crucial for implementing timely treatments, thereby reducing crop loss. Traditional manual diagnosis often suffers from low accuracy, high costs, and time consumption. To address these issues, this study introduces a method for identifying tomato pests and diseases using an improved residual network and transfer learning. A dataset comprising images of seven common tomato diseases and healthy leaves was created. This study introduces an improved residual network and transfer learning method to accurately identify tomato pests and diseases. The enhanced ResNet50 model, with an attention mechanism and focal loss, achieved 98.10% recognition accuracy. This research not only facilitates early disease detection, reducing crop loss but also minimizes pesticide use, thereby enhancing environmental sustainability and agricultural productivity worldwide.
Telemedicine and AI in Occupational Skin Disease Management: A Contemporary Review Purwoko, Reza Yuridian; Muliadi, Jemie; Roestam, Rusdianto; Wan Sen, Tjong Wan Sen; Pamungkas, Lukas Sangka; Nugroho, Anto Satriyo; Armi, Nasrullah; Supriyadi, Muhamad Rodhi; Melati, Rima; Alfaqih, Muhammad Subhan; Montolalu, Ivan Adrian; Ruhdiat, Rudi; Ferianasari, Inneke Winda; Aryanti, Evy Aryanti; Saputra, Silvan; Asmail, Asmail; Rahayaan, Manuela; Hi Rauf, Siti Nuraini
FIRM Journal of Management Studies Vol 8, No 2 (2023): FIRM JOURNAL OF MANAGEMENT STUDIES
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/firm.v8i2.5802

Abstract

Occupational skin diseases present significant challenges to workplace health, impacting both productivity and quality of life. The integration of telemedicine and artificial intelligence (AI) has transformed dermatological care by facilitating remote consultations, enabling early diagnosis, and supporting continuous monitoring. The COVID-19 pandemic has accelerated the adoption of digital health solutions, underscoring their potential to enhance accessibility and efficiency in occupational dermatology.AI-driven innovations, including machine learning algorithms and wearable technologies, have further improved diagnostic accuracy and patient management. However, challenges such as healthcare disparities, technological limitations, and workplace-specific factors continue to hinder widespread implementation. This review explores the evolving role of telemedicine and AI in managing occupational skin diseases, highlighting key challenges, emerging opportunities, and policy considerations for enhancing workplace health outcomes.
Optimizing KNN Algorithm Using Elbow Method for Predicting Voter Participation Using Fixed Voter List Data (DPT) Maulana, Ikmal; Roestam, Rusdianto
Jurnal Sosial Teknologi Vol. 4 No. 7 (2024): Jurnal Sosial dan Teknologi
Publisher : CV. Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsostech.v4i7.1308

Abstract

The purpose of this study is to produce maximum predictions for election participation rates. KNN (K-Nearest Neighbors) is one of the machine learning algorithms used to classify or regress data. The KNN algorithm works by finding the closest K training data from test data to be classified. Although the KNN (K-Nearest Neighbors) algorithm has advantages such as being easy to implement and being able to handle non-linear data, this algorithm also has several weaknesses, one of which is the determination of the value of K which is very ordinary and subjective. Therefore in this study optimization of the value of K on KNN using the Elbow method. The dataset used is the Fixed Voters List (DPT) in the 2019 General Elections in Karawang Regency. The final results of the experiments in this study, the highest achievement was obtained with a Mean Squared Error (MSE) value of 0.0018, a Root Mean Squared Error (RMSE) value of 0.0422, and a Mean Absolute Percent Error (MAPE) value of 6.36%. The highest accuracy produced in this study was 95.63% and the lowest was 93.64% with an average accuracy of 95.02%.
Co-Authors Afrina, Afrina Alfaqih, Muhammad Subhan Andika, Inno Reza Andres, M Anto Satriyo Nugroho, Anto Satriyo Aryanti, Evy Aryanti Asmail, Asmail Bawono, Bimo Adi christian christian Christina Christina ChungKiat, Ong Ediansa, Oka Fahmi, Hasanul Ferianasari, Inneke Winda Gulo, Trisnawati Hariyanto, Dian Tri Heri Suryadi Hernando, Irham Hi Rauf, Siti Nuraini Indahsari, Ayu Nur Irawan, Dodi Kurniawan, Alhadi Qori Lianzhai, Duan Lila Setiyani Linli, Peng Maulana, Ikmal Montolalu, Ivan Adrian Muliadi, Jemie Najuantah, Putra Nasrullah Armi Nasution, Wahyudi Nori Sahrun, Nori Pamungkas, Lukas Sangka Purwoko, Reza Yuridian Putra, Sumpena Adi Rahayaan, Manuela Rima Melati ROBY SETIAWAN Rosalina Rosalina Ruhdiat, Rudi Santriono, Refki Saputra, Silvan Sarjon Defit Siregar, Rizky Maulana Supriyadi, Muhamad Rodhi Suwarto Suwarto Suyanti Suyanti Tjong Wan Sen Triadi, Albet Wahyu, R. B. Wahyu, Raden Bagus Wan Sen, Tjong Wan Sen Yunarti, Welaspen Yusnita, Lita