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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 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) 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
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

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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

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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.
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.
Analysis of the Causal Relationship of Body Image Factors in Patients with Cancer Vita Ari Fatmawati; Christantie Effendy; Ridho Rahmadi
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.14287

Abstract

Patients with cancer can potentially experience the negative impacts of treatment. Physical conditions due to illness and therapy can affect the patient's body image. This study aims to find a causal model among body image factors of patients with cancer using the S3C-Latent Method. The measurement of body image of patients with cancer used the BIS questionnaire. One hundred and ninety-nine patients with cancer participated in this study. The results showed the existence of causal relationships between behavior to cognitive factors and duration of illness with reliability scores of 0.8 and 0.6, respectively; from gender to affective factors, illness duration, behavior, and cognitive factors with reliability scores of 0.6, 0.8, 0.65, and 1, respectively. There are also causal relationships from age to affective factors, duration of illness, and cognitive factors with reliability scores of 0.8, 0.7, and 0.9, respectively. The results also showed that affective factors are associated with behavior, cognitive factors, and duration of illness, with reliability scores of 1, 1, and 0.9, respectively. The results showed further the association of cognitive factors and illness duration with a reliability score of 1. We expect that the estimated causal model will serve as a scientific reference for medical experts in developing a better intervention such as treatment.
Decision Support System for Heart Disease Diagnosing Using K-NN Algorithm Tito Yuwono; Noor Akhmad Setiawan; Adi Nugroho; Anugrah Galang Persada; Ipin Prasojo; Sri Kusuma Dewi; Ridho Rahmadi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (977.661 KB) | DOI: 10.11591/eecsi.v2.776

Abstract

Heart disease is a notoriously dangerous disease whichpossibly causing the death. An electrocardiogram (ECG) is used fora diagnosis of the disease. It is often, however, a fault diagnosis by adoctor misleads to inappropriate treatment, which increases a riskof death. This present work implements k-nearest neighbor (K-NN)on ECG data to get a better interpretation which expected to help adecision making in the diagnosis. For experiment, we use an ECGdata from MIT BIH and zoom in on classification of three classes;normal, myocardial infarction and others. We use a single decisionthreshold to evaluate the validity of the experiment. The resultshows an accuracy up to 87% with a value of K = 4
Causal Modeling of Self Burden, Sosial Support, Spiritual Needs with CRF Using S3C-Latent Putri Mentari Endraswari; Ridho Rahmadi; Christantie Effendy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 6 (2020): Desember 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (773.891 KB) | DOI: 10.29207/resti.v4i6.2577

Abstract

Cancer patients experience cancer-related fatigue (CRF) that are subjective and persistent. CRF can have a negative impact on psychosocial, spiritual, and self-perceived burden. To understand more deeply about CRF, we need to answer one fundamental question: how are the causal mechanisms (cause-effect) of the factors related to CRF. The studies related so far are still limited to correlation analysis between factors and have not focused on the mechanism of a causal relationship. The purpose of this study is to model the causal relationship between CRF and psychosocial, spiritual, and self-perceived burden, using a causal method called the Stable Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). The results of this study are in the form of causal modeling between factors where self-burden has a causal relationship with CRF, spiritual need factors (religion) also have a causal relationship with CRF. Meanwhile, the social support factor (friends) with spiritual needs (religion) does not represent a causal relationship, but there is a strong association relationship. Meanwhile, the social support factor (friends) with CRF did not have a causal relationship or an association relationship between the two variables.
Sebuah Tinjauan Pustaka dari Studi-Studi Terkini Tentang Sistem Manajemen Lampu Lalu Lintas Adaptif Muhammad Sauqi Khatami; Rian Adam Rajagede; Ridho Rahmadi
AUTOMATA Vol. 2 No. 1 (2021)
Publisher : AUTOMATA

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Abstract

Kajian pustaka ini berusaha untuk mempelajari dan menganalisis penelitian-penelitian sebelumnya yang terkait dengan permasalahan pengaturan lampu lalu lintas adaptif. Setelah mengkaji sebelas literatur terkait pengaturan lampu lalu lintas adaptif, diketahui bahwa terdapat beberapa metode yang dapat melakukan pengaturan lampu lalu lintas adaptif beserta hasil dari masing-masing metode. Selain itu, ditemukan juga bagaimana representasi kepadatan lalu lintas dalam satu ruas jalan dan data yang digunakan dalam masing-masing penelitian.
Co-Authors Adi Nugroho Ahmad Fathan Hidayatullah, Ahmad Fathan Ahmad Rois Syujak Anugrah Galang Persada Christantie Effendy Dimas Ariyoga Fatih Assidhiqi Ipin Prasojo Muhammad Naufal Bahreisy Muhammad Sauqi Khatami Noor Akhmad Setiawan Nuli Giarsyani Perry Groot Putri Mentari Endraswari Putri Mentari Endraswari Rajagede, Rian Adam 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