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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi dan Manajemen Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jiko (Jurnal Informatika dan komputer) Indonesian Journal of Applied Informatics Jurnal Informatika dan Rekayasa Elektronik Automata Jurnal Generic
Ridho Rahmadi
Department Of Informatics, Faculty Of Industrial Technology, Universitas Islam Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Rancang Bangun Sistem Untuk Manajemen Barang Bukti Fisik dan Chain of Custody (CoC) pada Penyimpananan Laboratorium Forensika Digital Tino Feri Efendi; Ridho Rahmadi; Yudi Prayudi
Jurnal Teknologi dan Manajemen Informatika Vol 6, No 2 (2020): Desember 2020
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v6i2.4177

Abstract

Kejahatan komputer memiliki 2 jenis barang bukti, yaitu: bukti fisik dan bukti digital. Penyimpanan pada bukti fisik membutuhkan sebuah ruang khusus yang dapat menampung bukti fisik tersebut. Namun dibutuhkan sebuah sistem yang dapat menyimpan dan mengelola bukti fisik tersebut. Permasalahan yang ada saat ini adalah tidak adanya konsep penyimpanan bukti fisik serta dokumentasinya (Chain of Custody). Manajemen Barang Bukti Fisik diusulkan sebagai solusi untuk memecahkan masalah tersebut. Konsep ini berupa sebuah Sistem Manajemen Bukti Fisik dan Chain of Custody dengan mengambil analogi sebuah Data Inventory. Sedangkan informasi Chain of Custody. Permasalahan pada Manajemen Barang Bukti Fisik tersebut membutuhkan Sistem Manajemen untuk Barang Bukti Fisik yang sesuai untuk diterapkan dilingkungan Laboratorium Forensika Digital UII. Penelitian ini telah berhasil mengimplementasikan konsep Data Inventory. Diharapkan dengan adanya konsep Manajemen Barang Bukti Fisik ini kontrol barang bukti fisik dan segala aktivitas yang berkaitan dengannya dapat terjaga serta terdokumentasi dengan baik. DOI: https://doi.org/10.26905/jtmi.v6i2.4177
Mi-Botway: a Deep Learning-based Intelligent University Enquiries Chatbot Yurio Windiatmoko; Ahmad Fathan Hidayatullah; Dhomas Hatta Fudholi; Ridho Rahmadi
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (478.614 KB) | DOI: 10.29099/ijair.v6i1.247

Abstract

Intelligent systems for universities that are powered by artificial intelligence have been developed on a large scale to help people with various tasks. The chatbot concept is nothing new in today's society, which is developing with the latest technology. Students or prospective students often need actual information, such as asking customer service about the university, especially during the current pandemic, when it is difficult to hold a personal meeting in person. Chatbots utilized functionally as lecture schedule information, student grades information, also with some additional features for Muslim prayer schedules and weather forecast information. This conversation bot was developed with a deep learning model adopted by an artificial intelligence model that replicates human intelligence with a specific training scheme. The deep learning implemented is based on RNN which has a special memory storage scheme for deep learning models, in particular in this conversation bot using GRU which is integrated into RASA chatbot framework. GRU is also known as Gated Recurrent Unit, which effectively stores a portion of the memory that is needed, but removes the part that is not necessary. This chatbot is represented by a web application platform created by React JavaScript, and has 0.99 Average Precision Score.
Causal Relations of Factors Representing the Elderly Independence in Doing Activities of Daily Livings Using S3C-Latent Algorithm Nurhaeka Tou; Ridho Rahmadi; Christantie Effendy
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (695.906 KB) | DOI: 10.29099/ijair.v5i1.206

Abstract

The growth of the elderly population in Indonesia from year to year has always increased, followed by the problem of decreasing physical strength and psychological health of the elderly. These problems can affect the increase in dependence and decrease the independence of the elderly in ADL. In previous studies, various factors affect independence in ADLs such as cognitive, psychological, economic, nutrition, and health. However, In general, these studies only focus on predictive analysis or correlation of variables, and no research has attempted to identify the casual relationship of the elderly independence factors. Therefore, this study aimed to determine the mechanism of the causal relationship of the factors that influence the independence of the elderly in ADLs using a casual method called the Stable Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). In this research we found strong causal and associative relationships between factors.The causal relationship of elderly independence in ADLs was influenced by cognitive, psychological, nutritional and health factors and gender with α values respectively (0.61; 0.61;1.00, 0.65;0.70). Cognitive factors associated with psychological, economic, nutrition, and health with a value of α (0.77; 1.00; 1.00; 0.64). Furthermore, psychological factors associated with economy, nutrition, and health with a value of α (0.77; 0.95; 0.63). Bisides, economic factors are associated with nutrition and health with α values of ( 0.86; 0.75) and nutrition with health with α values of 0.64. The last association was found between nutritional factors and gender with a value of α 0.76. This research is expected to increase the independence of the elderly in carrying out daily activities.
A Mobile Deep Learning Model on Covid-19 CT-Scan Classification Prastyo Eko Susanto; Arrie Kurniawardhan; Dhomas Hatta Fudholi; Ridho Rahmadi
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : International Journal of Artificial Intelligence Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (386.607 KB) | DOI: 10.29099/ijair.v6i1.257

Abstract

COVID-19 pandemic is currently happening in the world. Previous studies have been done to diagnose COVID-19 by identifying CT-scan images through the development of the novel Joint Classification and Segmentation System models that work in real-time. In this study, the author focuses on a different motivation and innovation focused on the development of mobile deep learning. Mobile Net, a deep learning model as a method for classifying the disease COVID-19, is used as the base model. It has a good level of efficiency and reliability to be implemented on devices that have small memory and CPU specifications, such as mobile phones. The used data in this study is a CT-scan image of the lungs with a horizontal slice that has been classified as positive or negative for COVID-19. To give a broader analysis, the author compares and evaluates the model against other architectures, such as MobileNetV3 Large, MobileNetV3 Small, MobilenetV2, ResNet101, and EfficientNetB0. In terms of the developed mobile architecture model, the classification of COVID-19 using MobileNetV2 obtained the best result with 0.81 accuracy.
IMPLEMENTASI METODE GENERATE AND TEST DALAM MENYELESAIKAN TRAVELLING SALESMAN PROBLEM MENGGUNAKAN ROBOT BERSENSOR SONAR DAN WARNA Ridho Rahmadi
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2010
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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

Abstract

Masalah pencarian dan pelacakan merupakan hal penting dalam menentukan keberhasilan sebuah sistem yangberdasarkan Kecerdasan Buatan. Salah satu yang cukup dikenal adalah metode Generate and Test yangmerupakan satu dari beberapa model pencarian heuristik dalam terminologi Kecerdasan Buatan. TravellingSalesman Problem (TSP) atau juga dipahami sebagai pencarian jalur terpendek sering diimplementasikandalam dunia nyata seperti permasalahan distribusi produk perusahaan, pembuatan jaringan kabel telepon, danpembuatan PCB dalam dunia elektronika. Tujuan penelitian ini adalah mencoba mengimplementasikan konseppencarian heuristik dengan metode Generate and Test melalui sebuah robot yang dilengkapi sensor sonar untukmembaca jarak, dan sensor warna untuk membaca jalur sehingga dapat menemukan jalur terpendek dalamkasus TSP. Salah satu alasan mengapa menggunakan robot adalah selain melihat perkembangan implementasiKecerdasan Buatan yang telah meluas ke ranah robotika, penulis juga mencoba membuat bentuk lain daripenyelesaian TSP ini. Dari hasil penelitian ini didapatkan sebuah robot cerdas yang dapat membaca jarak antartitik menggunakan sensor sonar kemudian mengkalkulasi lintasan terpendek dan pada akhirnya melintasinyadengan membaca jalur menggunakan sensor warna.Kata Kunci: pencarian heuristik, generate and test, travelling saleman problem, robot, sensor sonar, sensorwarna.
Stable Specification Searches in Structural Equation Modeling Using a Multi-objective Evolutionary Algorithm Ridho Rahmadi; Perry Groot; Tom Heskes
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2014
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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

Abstract

Structural equation modelling (SEM) is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions [1]– [3]. SEM allows for both confirmatory and exploratory modeling. In exploratory modeling one starts with the specification of a hypothesis, which is tested against measurements by measuring how well the model fits the data. In exploratory modeling one searches the model space without stating a prior hypothesis. Exploratory modeling has the benefit that no prior background knowledge is needed, but has the drawback that the model search space grows super-exponentially since for n variables the number of SEM models is n4n. In the present paper we use an evolutionary algorithm approach to deal with the large search space in order to obtain good solutions within a reasonable amount of computation time. In addition, instead of dealing with one objective, we deal with multiple objectives to obtain more robust specifications. For this we employ the multi-objective evolutionary algorithm (MOEA) approach by using the Non- Dominated Sorting Genetic Algorithm-II (NSGA-II). At the end, to confirm the stability of a specification, we employ a stability selection approach. We validate our approach on a data set which is generated from an artificial model. Experimental results show that our procedure allows for stable inference of a causal model.
KOMPARASI ALGORITMA MACHINE LEARNING DAN DEEP LEARNING UNTUK NAMED ENTITY RECOGNITION : STUDI KASUS DATA KEBENCANAAN Nuli Giarsyani; Ahmad Fathan Hidayatullah; Ridho Rahmadi
Jurnal Informatika dan Rekayasa Elektronik Vol. 3 No. 1 (2020): JIRE April 2020
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v3i1.222

Abstract

Penelitian ini bertujuan untuk melakukan Named Entity Recognition guna mengidentifikasi dan mengklasifikasi kata pada tweet yang memuat informasi bencana ke dalam entitas-entitas yang telah ditentukan. Entitas yang diidentifikasi yaitu jenis bencana, lokasi, waktu, magnitude dan others. Adapun algoritma klasifikasi yang digunakan adalah Machine Learning dan Deep Learning. Algoritma Deep Learning yang digunakan yaitu Long Short-Term Memory, Gated Recurrent Units, dan Convolutional Neural Network. Sedangkan algoritma Machine Learning yang digunakan yaitu Naïve Bayes, Decision Tree, Support Vector Machine dan Random Forest. Berdasarkan hasil eksperimen, Deep Learning memperoleh akurasi yang lebih unggul dari Machine Learning. Hal tersebut dilihat dari perolehan nilai accuracy terbaik Deep Learning dihasilkan dari algoritma Gated Recurrent Units dan Long Short-Term Memory dengan nilai 0.999. Sedangkan perolehan accuracy terbaik Machine Learning dihasilkan dari algoritma Random Forest sebesar 0.98.
Pemodelan Kausal Faktor-Faktor Beban Keluarga dalam Merawat Pasien Kanker Menggunakan Algoritma S3C-Latent Rizki Surtiyan Surya; Christantie Effendy; Ridho Rahmadi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814266

Abstract

Pasien kanker memiliki kebutuhan yang kompleks mulai dari masalah fisik, psikologis, sosial dan spiritual. Keluarga yang merawat pasien kanker disebut family caregiver. Seorang family caregiver membantu mengatasi hampir semua permasalahan yang dialami pasien baik saat dirawat di rumah maupun di rumah sakit. Keluarga mengalami suka dan duka dalam merawat pasien. Dalam merawat pasien dengan penyakit kronis, bukan hanya pasien tetapi kesejahteraan dan kualitas hidup family caregiver juga penting. Oleh karena itu sangat penting untuk mengetahui bagaimana beban family caregiver dan faktor-faktor yang mempengaruhi beban keluarga dalam merawat pasien.  Beban family caregiver dapat diukur menggunakan Caregiver Reaction Assesment (CRA), yang direpresentasikan oleh beberapa faktor. Dengan memahami hubungan kausal antara faktor-faktor beban keluarga, diharapkan dapat membantu untuk mengidentifikasi bagaimana beban caregiver bersumber dan berdampak. Untuk itu, penelitian ini bertujuan untuk mengidentifikasi hubungan kausal antara faktor-faktor yang berhubungan dengan beban family caregiver dalam merawat pasien. Penelitian ini menggunakan algoritma pemodelan kausal bernama Stable Specification Search for Cross-sectional Data with Latent Variable (S3C-Latent) untuk mendapatkan model kausal antara faktor-faktor beban family caregiver yang relevan. Dari hasil analisis  pemodelan  didapatkan ada 3 faktor yang memiliki hubungan kausal dan 2 faktor memiliki hubungan asosiasi. Gender memiliki hubungan kausal yang stabil terhadap kesiapan kesehatan dan kesiapan dalam merawat. Sedangkan faktor kesiapan merawat mempengaruhi faktor aktivitas family caregiver, selain itu faktor keuangan memiliki hubungan asosiasi yang kuat dengan faktor aktivitas dan hubungan keluarga. Pemodelan kausal ini dapat digunakan sebagai acuan bagi tenaga kesehatan dalam pelayanan kesehatan yang lebih tepat, efisien, dan efektif di dalam menangani permasalahan beban caregiver. AbstractCancer patients have complex needs ranging from physical, psychological, social, and spiritual problems. Families who take care for cancer patients are called family caregivers. A family caregiver helps to overcome almost all problems experienced by the patients both while being treated at home and in the hospital. Families experience joy and sorrow in caring for patients. In treating patients with chronic diseases, not only the patient but the family caregiver's well-being and quality of life are also important. Therefore, it is very important to know how the family caregiver's burden is and the factors that affect the family burden in caring for patients. Caregiver family burden can be measured using Caregiver Reaction Assessment (CRA), which is represented by several factors. By understanding the causal relationship between family burden factors, it is hoped that it can help to identify how the caregiver burden is sourced and impacted. Therefore, this study aims to identify the causal relationships between factors related to the burden on family caregivers in caring for patients. This study uses a causal modeling algorithm called Stable Specification Search for Cross-sectional Data with Latent Variable (S3C-Latent) to obtain a causal model between the relevant caregiver family load factors. The results of modeling analysis showed that there are 3 factors which have a causal relationship and 2 factors have an association relationship. Gender has a stable causal relationship to health readiness and readiness to care, Moreover, the caring readiness factor affects the family caregiver activity factor, and the financial factor has a strong association with the activity factor and family relationships. This causal modeling can be used as a reference for health workers so as to give health services which are precise, efficient, and effective in dealing with caregiver burden problems.
Causal Relationships of Sexual Dysfunction Factors in Women Using S3C-Latent Yuan Sa'adati; Christantie Effendy; Ridho Rahmadi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 1 (2021): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.62144

Abstract

Women with cancer are at risk for sexual dysfunction characterized by problems with sexual desire, sexual arousal, lubrication, orgasm, sexual satisfaction, and pain during sexual intercourse. The literature review shows that most studies have focused on correlation analysis between factors, and no studies have attempted to identify a causal relationship between factors of sexual dysfunction. This study aims to determine the causal mechanism between factors of sexual dysfunction in cancer patients using a causal algorithm called the Stablespec Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). The causal algorithm has been implemented into the R software package called Stablespec. The computation of the model is done in parallel using the CPU server. The result of this study is that there are a causal relationship and association with a high-reliability score of sexual dysfunction factors. We hope that the causal model obtained can be a scientific reference for doctors and health workers in making decisions so that the quality of life of female cancer patients who experience sexual dysfunction can be improved.
Causal Modeling Between Factors on Quality of Life in Cancer Patients Using S3C-Latent Algorithm Yohani Setiya Rafika Nur; Ridho Rahmadi; Christantie Effendy
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.74-83

Abstract

Background: Cancer patients can experience both physical and non-physical problems such as psychosocial, spiritual, and emotional problems, which impact the quality of life. Previous studies on quality of life mostly have employed multivariate analyses. To our knowledge, no studies have focused yet on the underlying causal relationship between factors representing the quality of life of cancer patients, which is very important when attempting to improve the quality of life.  Objective: The study aims to model the causal relationships between the factors that represent cancer and quality of life.Methods: This study uses the S3C-Latent method to estimate the causal model relationships between the factors. The S3C-Latent method combines Structural Equation Model (SEM), a multi objective optimization method, and the stability selection approach, to estimate a stable and parsimonious causal model.Results: There are nine causal relations that have been found, i.e., from physical to global health with a reliability score of 0.73, to performance status with a reliability score of 1, from emotional to global health with a reliability score of 0.71, to performance status with a reliability score of 0.82, from nausea, loss of appetite, dyspnea, insomnia, loss of appetite and from constipation to performance status with reliability scores of 0.76; 1; 0.61; 0.76; 0.72; 0.70, respectively. Moreover, this study found that 15 associations (strong relation where the causal direction cannot be determined from the data alone) between factors with reliability scores range from 0.65 to 1.Conclusion: The estimated model is consistent with the results shown in previous studies. The model is expected to provide evidence-based recommendation for health care providers in designing strategies to increase cancer patients’ life quality. For future research, we suggest studies to include more variables in the model to capture a broader view to the problem.
Co-Authors Adi Nugroho Ahmad Fathan Hidayatullah, Ahmad Fathan Ahmad Rois Syujak Anugrah Galang Persada Arrie Kurniawardhan Christantie Effendy Dhomas Hatta Fudholi Dimas Ariyoga Fatih Assidhiqi Ipin Prasojo Muhammad Naufal Bahreisy Muhammad Sauqi Khatami Noor Akhmad Setiawan Nuli Giarsyani Nurhaeka Tou Perry Groot Prastyo Eko Susanto Putri Mentari Endraswari Putri Mentari Endraswari Rajagede, Rian Adam Rizki Surtiyan Surya Sri Kusuma Dewi Tino Feri Efendi Tito Yuwono, Tito Tom Heskes Vita Ari Fatmawati Yohani Setiya Rafika Nur Yuan Sa'adati Yudi prayudi Yundari, Yundari Yurio Windiatmoko