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Makassar Smart City Operation Center Priority Optimization Using Fuzzy Multi-Criteria Decision- Making Fachrul Kurniawan; Aji Prasetya Wibawa; Munir Munir; Supeno Mardi Susiki Nugroho; Mochamad Hariadi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.284 KB) | DOI: 10.11591/eecsi.v4.1010

Abstract

The development of smart city operation room of Makassar possesses several equally important stages which are equally important. There are four stages of development that are 1) data center construction, 2) camera distribution around the city , 3) wall room monitoring construction, and 4) smart operation room architecture construction. Since the time and cost are limited, it forces the project manager to be able to manage and  control the  priority in  conducting the  project. There are several usable criteria to determine the priority in conducting the project development through criteria consideration of the entire project stages. Project priority optimization system aims at making every single project activity effective including its evaluation process. It also exposes a ranking illustration of foremost project priority by providing cost preference of the entire development stages. Fuzzy Multi-Criteria Decision-Making is used to illustrate the project priority rank and further to determine the alternative optimal option in conducting the project. This enforces particular project to  allocate its  cost to  the  project  with  a higher level of cost necessity. The company, therefore, enables to make effective funding for the entire project based on the level of importance and time achievement and subsequently it promotes  accessible  technology integration.  The  conducted experiment suggests that the first construction of the project is data center construction followed by wall room construction and  CCTV  distribution. This  is  relevant  with  optimization value result of data center 0,405 higher than A2 0,42 for wall room construction and A3 CCTV distribution 0,24.
Opinion Detection of Public Sector Financial Statements Using K-Nearest Neighbors Ahmad Dwi Arianto; Achmad Affandi; Supeno Mardi Susiki Nugroho
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (248.178 KB) | DOI: 10.11591/eecsi.v4.1113

Abstract

The identification of ethical violations committedby the auditor is very difficult to do. Artificial intelligence offersanomaly detection as an alternative method for detecting theopinion anomaly which can be an early indicator of the opiniontrading occurrence. This paper proposes the use of originalfeatures from public sector rather than the use of modifiedfeatures from the private sector to be applied in opinion detectionin public sector. By using 60% Holdout validation, 1-NNclassification showed that original featured from the public sectoroutperformed the modified featured from the private sector by5.82% through 13.10% under F-Measure Criterion and by4.22% through 9.56% under AUC criterion.
An Opinion Anomaly Detection Using K-Nearest Neighbours on Public Sector Financial Reports Ahmad Dwi Arianto; Achmad Affandi; Supeno Mardi Susiki Nugroho
IPTEK Journal of Proceedings Series No 1 (2018): 3rd International Seminar on Science and Technology (ISST) 2017
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2018i1.3498

Abstract

The Main Inspectorate (Itama) as internal auditor of BPK RI is obliged to protect the credibility and the honor of its institution. The opinion of financial statements is one of the BPK RI's products that become popular because of frequent bribery cases related to it. Typically, the bribe was given to change the opinion of the financial statements from an examined entity. The anomaly detection method becomes one of the alternative methods for filtering out reports with "problem" opinions to be examined more deeply by Itama. KNN, SVM-RBF Kernel, and J48 method were used for the classification of 150 data of local government financial statements. The validation used in this paper was 60% hold-out validation (60% data for test data and the rest for training data). This paper showed that the KNN classifier (AUC=61.11%) was superior compared to another classifier, but still classified as "poor classification"
Interaksi 3D Sensor Leap Motion untuk Menggenggam Benda Virtual Lukman Hakim; Surya Sumpeno; Supeno Mardi Susiki Nugroho
CYCLOTRON Vol 3, No 2 (2020): CYCLOTRON
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (352.605 KB) | DOI: 10.30651/cl.v3i2.5674

Abstract

Abstrak - Penelitian ini membahas tentang interaksi 3D sensor Leap Motion untuk simulasi menggenggam Benda virtual Plastis. Sebuah interaksi 3D sensor Leap Motion yang digunakan sebagai simulasi untuk menggenggam benda virtual Plastis dengan menggunakan media objek telur virtual secara presisi dan akurasi yang tepat. Pada dasarnya menggenggam merupakan suatu kegiatan yang menerapkan kinerja motorik halus pada tangan untuk melakukan gerakan. Penggunaan sensor Leap Motion sebagai interaksi 3D dipakai untuk menggenggam objek maya dalam hal ini bentuk 3D telur virtual sebagai media praktiknya. Telur sendiri merupakan benda yang gampang distimulasi dan memiliki sifat texture yang halus untuk merespon segala bentuk gerakan pada genggaman tangan. Dalam penelitian Interaksi 3D Sensor Leap Motion untuk simulasi untuk menggenggam benda Virtual Plastis dengan menggunakan media objek telur virtual, ini di peruntukkan untuk mengetahui akurasi dan presisi dari pola gerakan tangan secara imersif. Pengembangan dari metode ini bertujuan untuk simulasi menggenggam benda atau objek maya dengan adanya interaksi pola gerakan tangan.Kata kunci: leapmotion, 3d, virtual reality, benda, telurAbstract - This study discusses about the 3D interaction of the Leap Motion sensor for the simulation of holding virtual plastic objects. A 3D interaction of the Leap Motion sensor that is used as a simulation to hold Plastis virtual objects by using virtual egg object media with precise and right accuracy. Basically, holding is an activity that applies fine motor performance on the hands to make movements. The use of the Leap Motion sensor as a 3D interaction is used to hold virtual objects in this case a 3D form of virtual eggs as practice media. Eggs are objects that are easily stimulated and have subtle texture to respond to all forms of movement in the hands. In the 3D interaction Leap Motion Sensors for virtual plastic objects holding simulation by using virtual egg object media, it is intended to find out the accuracy and precision of immersive hand movement patterns. The development of this method aims to simulate holding virtual objects or objects with the interaction of hand movement patterns.Keywords: leap motion, 3d, virtual reality, object, egg
Rule-based Disease Classification using Text Mining on Symptoms Extraction from Electronic Medical Records in Indonesian Alfonsus Haryo Sangaji; Yuri Pamungkas; Supeno Mardi Susiki Nugroho; Adhi Dharma Wibawa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 1, February 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i1.1377

Abstract

Recently, electronic medical record (EMR) has become the source of many insights for clinicians and hospital management. EMR stores much important information and new knowledge regarding many aspects for hospital and clinician competitive advantage. It is valuable not only for mining data patterns saved in it regarding the patient symptoms, medication, and treatment, but also it is the box deposit of many new strategies and future trends in the medical world. However, EMR remains a challenge for many clinicians because of its unstructured form. Information extraction helps in finding valuable information in unstructured data. In this paper, information on disease symptoms in the form of text data is the focus of this study. Only the highest prevalence rate of diseases in Indonesia, such as tuberculosis, malignant neoplasm, diabetes mellitus, hypertensive, and renal failure, are analyzed. Pre-processing techniques such as data cleansing and correction play a significant role in obtaining the features. Since the amount of data is imbalanced, SMOTE technique is implemented to overcome this condition. The process of extracting symptoms from EMR data uses a rule-based algorithm. Two algorithms were implemented to classify the disease based on the features, namely SVM and Random Forest. The result showed that the rule-based symptoms extraction works well in extracting valuable information from the unstructured EMR. The classification performance on all algorithms with accuracy in SVM 78% and RF 89%.
Prediction of Students' Ability to Difficulty Level of Problem Based on Linear Method Hervit Ananta Vidada; Eko Mulyanto Yuniarno; Supeno Mardi Susiki Nugroho; Umi Laili Yuhana
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 5 No 2 (2021): October
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v5i2.1459

Abstract

Knowing the ability of students is something that is important to formulate exam questions correctly, namely questions with the appropriate level of difficulty. However, in general, exam questions are prepared with the assumption that students' abilities are the same, so the results obtained do not reflect the actual abilities of students. This study focuses on predicting the ability of grade 6 students in mathematics. The data was obtained from 400 exam questions with 8 materials done by 23 students. Students' ability categories are grouped into 3, namely high ability, medium ability, and low ability. The difficulty of the questions is grouped into difficult questions, medium questions, and easy questions based on the assessments of 5 different class teachers. Our research uses the linear regression method and successfully shows that there is a close relationship between students' abilities and the level of difficulty of the questions. The difficulty level of the questions contributed 63% to the students' abilities. The standard error of 0.04905 means that the regression model is the right model in determining students' abilities.
Crowd evacuation navigation for evasive maneuver of brownian based dynamic obstacles using reciprocal velocity obstacles Susi Juniastuti; Moch Fachri; Fresy Nugroho; Supeno Mardi Susiki Nugroho; Mochamad Hariadi
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3806

Abstract

This paper presents an approach for evasive maneuver against dynamic obstacles in multi-agent navigation in a crowd evacuation scenario. Our proposed approach is based on reciprocal velocity obstacles (RVO) with a different manner to treat the obstacles. We treat all possible hindrances in velocity space reciprocally thus all collision cones generated by other agents and obstacles are treated in the same RVO manner with the key difference in the effort of avoidance. Our approach assumes that dynamic obstacles bear no awareness of navigation space unlike agents thus the avoidance effort lies on behalf of the mobile agents, creating unmutual effort in an evasive maneuver. We display our approach in an evacuation scenario where a crowd of agents must navigate through an evacuation area trespassing zone filled with dynamic obstacles. These dynamic obstacles consist of random motion built based on Brownian motion thus posses an immense challenge for the mobile agent in order to overcome this hindrance and safely navigate to their evacuation area. Our experimentation shows that 51.1% fewer collisions occurred which is denote safer navigation for agents in approaching their evacuation point.
Inert and mobile agents navigation interaction using reciprocal velocity obstacles for collisions avoidance Susi Juniastuti; Moch Fachri; Supeno Mardi Susiki Nugroho; Mochamad Hariadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1116-1124

Abstract

Reciprocal velocity obstacles (RVO) is a method used for multiagents navigation that enables collision and oscillation-free avoidance against other mobile agents. Despite its ability in collision avoidance between agents, RVO has a hard time dealing with static obstacle avoidance. This problem has led to a tendency to use RVO only for agents avoidance and use other methods to handle static obstacles avoidance. In this paper, we present our new approach for interaction between mobile agents against static obstacles in the RVO based collision avoidance. We propose a concept called inert agents that interact as static obstacles. This inert agent is stand firm as static obstacles should be, while the inert agent also able to satisfy reactive collision avoidance nature of RVO to produce better avoidance result. We conduct an experiment to compare the performance of avoidance in a certain scenario. Our method shows better results when compared with generic static obstacles.
Enhancing the feature-based 3D deformable face recognition using hybrid PCA-NN Cahyo Darujati; Supeno Mardi Susiki Nugroho; Deny Kurniawan; Mochamad Hariadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp215-221

Abstract

Facial recognition is one of the most important advancements in image processing. An important job is to build an automated framework with the same human capacity’s for recognizing face. The face is a complex 3D graphical model, and constructing a computational model is a challenging task. This paper aims at a facial detection technique focused on the coding and decoding of the facial feature object theory approach to data. One of the most natural and common principal component analysis (PCA) method. This approach transforms the face features into a minimal set of basic attributes, peculiarities, which are the critical components of the original learning image collection (or the training package). The proposed technique is a combination of the PCA system and the identification of components using the neural network (NN) feed-forward propagation method. This experiment proves that recognition of deformed 3D face is doable. By taking into account almost all forms of feature extraction and engineering, the NN yields a recognition score of 95%.
Defense behavior of real time strategy games: comparison between HFSM and FSM Rahmat Fauzi; Mochamad Hariadi; Muharman Lubis; Supeno Mardi Susiki Nugroho
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp634-642

Abstract

RTS Game is one of the popular genre in PC gaming, which has been played by various type of players frequently. In RTS game, NPC Defense Building (Tower) has attacking behavior to the closest enemy without considering certain enemy parameters. This causes the NPC Tower to be more predictable by the opponent and easily defeated if NPC attacked by enemies in the group. Thus, this research simulates NPC Tower using Hierarchical Finite State Machine (HFSM) method compared with Finite State Machine (FSM). In this study, NPC Tower detects enemies by seeing at four parameters namely NPC Tower Health, Enemy's Health, Enemy Type, and Tower Distance to enemies. NPC Tower will attack the most dangerous enemy according to the ‘Degree of Danger’ parameter. Then use the decision-making logic of the rule-based system. The output of NPC Tower are three type of behaviors namely Aggressive Attacking, Regular Attacking, and Attack with Special Skill. From the test results of 3 NPC Tower, Kamandaka NPC Tower with HFSM method is winning 8.92% compare to Kamandaka Tower with FSM method. For Gayatri Tower NPC obtained equal results using both HFSM and FSM. Meanwhile, Adikara NPC with HFSM method is 4.62% superior to Adikara Tower with FSM method.