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All Journal INFORMAL: Informatics Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jurnal Teknologi dan Sistem Informasi Jurnal Sains Teknologi dan Sistem Informasi Jurnal Sains dan Teknologi
Erliyan Redy Susanto
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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Penerapan Metode Forward Chaining untuk Mendiagnosa Penyakit Tanaman Sawi Ajeng Savitri Puspaningrum; Erliyan Redy Susanto; Adi Sucipto
INFORMAL: Informatics Journal Vol 5 No 3 (2020): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v5i3.20237

Abstract

Mustard greens is one of the plants that is very susceptible to disease. There is no type of mustard greens plant that is strong against disease or pests. In order to overcome or treat these diseases and pests, mustard greens farmers need to know the type of disease or pest that is attacking. But unfortunately, farmers' knowledge regarding mustard plant diseases is limited to the visible symptoms or the condition of the affected plant. The lack of knowledge of mustard greens farmer practitioners is due to the lack of counseling so that the presence of experts to help farmers is needed. Unfortunately, the experts are not always able to help if the condition requires fast handling because the number of experts are not many. In this study, this problem was resolved by developing a system equivalent to expert knowledge in detecting diseases in mustard plants. The system developed was a web-based system to detecting types of mustard disease automatically with the forward chaining method which can be used as an alternative to manual activities carried out by experts using 9 pests/diseases and 18 symptoms. The test results showed that the level of accuracy with expert expertise reaches 88.8%
IMPLEMENTASI DASHBOARD SMART ENERGY UNTUK PENGONTROLAN RUMAH PINTAR PADA PERANGKAT BERGERAK BERBASIS INTERNET OF THINGS Syaiful Ahdan; Erliyan Redy Susanto
Jurnal Teknoinfo Vol 15, No 1 (2021): Januari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v15i1.954

Abstract

Internet of Things (IoT) adalah sebuah konsep yang dapat memperluas konektivitas yang terhubung dengan jaringan global, Penelitian ini bertujuan untuk membangun perangkat kendali yang memanfaatkan teknologi jaringan internet dengan menghubungkan sebuah perangkat melalui sistem pada perangkat seluler, kontribusi pada penelitian ini adalah membangun perangkat energi pintar yang diimplementasikan pada fitur rumah pintar yang telah dihubungkan dengan perangkat sensor sehingga peralatan elektronik dapat dikontrol sesuai dengan kondisi yang diinginkan secara otomatis atau dikendalikan langsung melalui aplikasi android. Berdasarkan hasil pengujian yang dilakukan pada dashboard smart energy bahwa pada aspek usability 89%, functionality 93% dan reliability 77%, hasil pada pengujian aspek efficiency tahap akhir pada dashboard smart energy memperolah tingkat efesiensi CPU tertinggi pada level 28% dan stabil pada persentasi 10% dengan efesiensi memori sebesar 47.7MB Kesimpulan dari penelitian ini adalah bahwa teknologi IoT dapat mempermudah dalam hal pengendalian perangkat listrik. 
RANCANG BANGUN WEBSITE SISTEM INFORMASI MANAJEMEN SEWA LAPANGAN FUTSAL STUDI KASUS DAMAI FUTSAL LAMPUNG Nindi Sekar Ayu; Erliyan Redy Susanto; Muhaqiqin Muhaqiqin
Jurnal Teknologi dan Sistem Informasi Vol 3, No 4 (2022): Volume 3 No. 4 December 2022
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtsi.v3i4.1548

Abstract

Penelitian ini dilakukan untuk membuat sebuah website atau sistem yang dapat melakukan proses penyewaan jadwal lapangan futsal. Sebelumnya Penyewa saat sedang ingin melakukan penyewaan harus dating langsung ke lokasi atau mengirimkan pesan whatapps kepada pengelola lapangan. Lokasi penelitian di Damai Futsal Lampung yang merupakan tempat penyewaan lapangan futsal yang berlokasi di kecamatan Kemiling, Bandar Lampung. Manfaat dari aplikasi ini adalah bagi Penyewa adalah mempermudah penyewa dalam melakukan penyewaan lapangan tanpa harus dating ke lokasi. Manfaat dari aplikasi ini bagi Pemilik dan Pengelola adalah membantu dalam pengelolaan manajemen segala aktivitas proses bisnis dan pemilik juga dapat lebih mudah melihat laporan hasil sewa lapangan tersebut. Dalam penelitian ini peneliti menggunakan metode pengembangan software yatiu metode Web Engineering. Dalam tahapan construction sistem dibuat dengan bahasa pemrograman PHP Laravel, Java dan database MySQL. Dalam penggunaanya penyewa akan dapat menggunakan aplikasi android yang berbasis WebView, sedangkan pengelola dan  pemilik menggunakan Website. Pada tahapan testing sistem ini menggunakan untuk aspek functionality menggunakan Black Box serta ISO 9126 untuk menguji aspek usability. Hasil dari tahapan testingFunctionality menghasilkan score penilaian 99,074% dengan hasil diterima secara kelayakan fungsi. Serta testingUsability menghasilkan score 95,873% dengan dengan sangat layak.
PENERAPAN SISTEM PENGARSIPAN DIGITAL SEBAGAI PENDUKUNG OPTIMALISASI PENGELOLAAN ADMINISTRASI PADA SUB BAGIAN UMUM BADAN PENDAPATAN DAERAH PROVINSI LAMPUNG desi damayanti; S.Samsugi S.Samsugi; Erliyan Redy Susanto
Jurnal Teknologi dan Sistem Informasi Vol 4, No 3 (2023): Volume 4 Nomer 3 September 2023
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtsi.v4i3.2916

Abstract

Sub Bagian Umum dan Kepegawaian merupakan bagian dari struktur organisasi Sekretariat di Badan Pendapatan Daerah Provinsi Lampung. Saat ini dalam mengelola kearsipan di Sub Bagian Umum dan Kepegawaian masih bersifat konvensional yaitu menggunakan buku agenda dalam pencatatannya dan lemari pengarsipan sebagai media penyimpanan dokumennya. Penyimpanan dokumen arsip secara konvensional tidak dapat menyimpan dokumen untuk jangka waktu yang lama, karena dapat menyebabkan penumpukan arsip dan kerusakan akibat tergerus waktu. Tujuan penelitian ini adalah Menghasilkan sistem pengarsipan digital sebagai pendukung optimalisasi pengelolaan adaministrasi Sub Bagian Umum dan Kepegawaian di Badan Pendapatan Daerah Provinsi Lampung. Metode pengembangan sistem yang digunakan adalah model Etreme Programming (XP) dengan pengujian menggunakan menggunakan ISO 25010. Perancangan sistem menggunakan UML yang terdiri dari perancangan usecase diagram, activity diagram, dan class diagram. Sedangkan proses koding program menggunakan framework Codeigniter, bahasa pemrograman PHP dan MySQL sebagai databse. Hasil penelitian ini adalah pengujian sistem memperoleh hasil 87.94% atau dibulatkan menjadi 88%. Sehingga dari hasil pengujian tersebut maka dapat disimpulkan bahwa perancangan sistem ini dinyatakan memenuhi syarat untuk di implementasikan dengan kategori sangat baik atau sangat layak.
Analisis Implementasi Sistem Keamanan Basis Data Berbasis Role-Based Access Control (RBAC) pada Aplikasi Enterprise Resource Planning M Sahyudi; Erliyan Redy Susanto
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 1 (2025): April 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i1.3997

Abstract

Role-Based Access Control (RBAC) has become the main approach in improving data security in various information systems. This study analyzes the implementation of RBAC in the context of Enterprise Resource Planning (ERP) applications and cloud-based, mobile, and multi-domain systems. Using a systematic literature review (SLR) methodology, this study synthesizes findings from various studies to evaluate the effectiveness of RBAC in addressing challenges such as data privacy, regulatory compliance, and access policy complexity. The results show that the integration of intelligent technologies, such as machine learning (decision tree and random forest algorithms) for user behavior analysis, natural language processing for policy interpretation, and blockchain to record access activities with a security increase of up to 37%, can increase the flexibility and efficiency of RBAC, especially in detecting anomalies and managing dynamic policies. In addition, automation in RBAC deployments has been proven to reduce operational costs by 42% and management time by up to 65% compared to traditional manual approaches. However, RBAC implementation also faces significant challenges, including the need to adapt to complex regulations and the dynamics of a multi-domain environment. This research makes a theoretical contribution by expanding the understanding of the role of RBAC in modern data security management and offering practical recommendations for optimizing RBAC implementation. Thus, RBAC has proven to be a relevant and reliable model in answering data security needs in the digital era. 
Hybrid XGBoost-SVM Model untuk Sistem Pendukung Keputusan dalam Prediksi Penyakit Diabetes Muhammad Surono; Muhammad Fadli; Dian Sri Purwanti; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5410

Abstract

Diabetes is a chronic disease that continues to rise globally each year, requiring early detection for more effective prevention. This study develops an artificial intelligence-based decision support system for diabetes prediction using a Hybrid XGBoost-SVM model. The model combines the Support Vector Machine (SVM), known for its interpretability, with XGBoost (XGB), which enhances accuracy through boosting techniques. The study utilizes the Pima Indians Diabetes Dataset, undergoing preprocessing, normalization, data splitting, and model training. The evaluation compares accuracy, precision, recall, and F1-score across the three models. Experimental results indicate that XGBoost and SVM both achieve an accuracy of 75%. However, the Hybrid XGBoost-SVM model provides consistently improved performance, achieving the highest accuracy (77%), along with increased precision (70%) and F1-score (65%). Although the numerical improvement in accuracy appears relatively small, this enhancement is significant in the medical context, especially due to improved precision and balanced classification. This study concludes that the Hybrid XGBoost-SVM approach offers a more optimal and reliable alternative in decision support systems for diabetes prediction. Future research can explore other model combinations, such as Stacking or Weighted Voting, to enhance predictive performance further.
Implementation of Deep Learning with Multilayer Perceptron (MLP) for Heart Disease Prediction Using the SMOTE-ENN Technique Erliyan Redy Susanto; Erik Saputra
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9337

Abstract

Heart disease is a leading cause of global mortality, with its prevalence increasing annually. This study aims to develop a heart disease prediction model using a Multilayer Perceptron (MLP) combined with the SMOTE-ENN resampling technique to address data imbalance issues. The dataset used was obtained from the UCI Machine Learning Repository and includes patients' clinical and demographic features. The initial dataset consisted of [number of data] records, with an imbalanced class distribution between patients with and without heart disease. After applying SMOTE-ENN, the class distribution became more balanced, allowing the model to learn patterns more effectively. The MLP model was designed with two hidden layers comprising 64 and 32 neurons, respectively, using the ReLU activation function in the hidden layers and a sigmoid function in the output layer. Evaluation results showed that the model achieved an accuracy of 89.47%, precision of 77.78%, recall of 100%, and an F1-score of 87.5%. To validate the effectiveness of SMOTE-ENN, comparisons were made with other methods such as SMOTE and undersampling, as well as baseline models like Logistic Regression and Decision Tree. The results demonstrate that SMOTE-ENN outperforms other techniques in handling class imbalance, leading to better overall model performance.
Co-Authors Adi Sucipto, Adi desi damayanti Dian Sri Purwanti Erik Saputra M Sahyudi Muhammad Fadli Muhammad Surono Muhaqiqin Muhaqiqin Nindi Sekar Ayu S. Samsugi Syaiful Ahdan