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Analisa dan Perancangan Aplikasi Pengajuan Cuti Pada PT. Mun Hean Indonesia Ali Khumaidi; Andrian Muljadi
Jurnal Inovtek Polbeng Seri Informatika Vol 5, No 1 (2020)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1189.212 KB) | DOI: 10.35314/isi.v5i1.1191

Abstract

Cuti merupakan salah satu kewajiban perusahaan yang diberikan kepada karyawan. Hal mengenai perizinan dan cuti tersebut juga telah diatur dalam Undang-Undang No. 13 tahun 2003 tentang ketenagakerjaan. Proses pengajuan cuti pada PT. Mun Hean Indonesia saat ini masih dilakukan secara manual, pengecekan sisa cuti, pengajuan cuti dan persetujuan cuti dilakukan secara berjenjang dan masih mengandalkan arsip cuti berupa kertas. Proses pengelolaan cuti tersebut memiliki beberapa kelemahan. Pegawai tidak bisa mengetahui sisa hak cuti pribadi dan kelemahan yang lain adalah proses pengajuan cuti kurang efektif dan efisien. Tujuan dari penulisan ini adalah untuk memperbaiki kekurangan yang ada pada sistem yang berjalan saat ini serta mempermudah dan mempercepat proses pengajuan cuti karyawan. Pada perancangan aplikasi cuti ini penulis menggunakan alat pemodelan berupa flow map, metode pendekatan sistem menggunakan metode berorientasi objek yaitu Use Case Diagram, Activity Diagram, Class Diagram dan Deployment Diagram, rancangan sistem basis data dan rancangan antarmuka pengguna.
DATA MINING FOR PREDICTING THE AMOUNT OF COFFEE PRODUCTION USING CRISP-DM METHOD Ali Khumaidi
Jurnal Techno Nusa Mandiri Vol 17 No 1 (2020): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (955.427 KB) | DOI: 10.33480/techno.v17i1.1240

Abstract

The production of coffee plantations has become the leading plantation commodity with the export value of the fourth rank after oil palm, rubber and coconut. The number of coffee needs for export every year always increases, therefore it is necessary to predict the yield of coffee plants to estimate planting and anticipation that will be done so as to achieve the target. Coffee plant productivity is influenced by internal and external factors, namely the quality of the plant itself, soil, altitude and climate. The method used in this study is the CRISP-DM method and multiple linear regression algorithm to predict the amount of coffee production and determine the relationship between the variables. The steps taken are business understanding, data understanding, data preparation, modeling and evaluation. The data set that is used as many as 170 data after going through the data preparation stage produced 150 data with 5 attributes in the table. With calculations using tools, the coefficient of determination is 91.96%. That the variation in the value of the production of coffee plants is influenced by independent variables, namely the area of ​​plantations, rainfall, air pressure and solar radiation by 91.96% and 8.04% influenced by other variables not measured in this study. The results of the evaluation and validation of predictions produce good accuracy with an RMSE value of 0.3477.
Simulation Of Traveling Salesman Problem For Distribution Of Fruits In Bogor City With Simulated Annealing Method: Simulation Of Traveling Salesman Problem For Distribution Of Fruits In Bogor City With Simulated Annealing Method Ali Khumaidi; Ridwan Raafi'udin; Indra Permana Solihin
Jurnal Mantik Vol. 3 No. 4 (2020): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.598 KB)

Abstract

Traveling Salesman Problem (TSP) is a problem of finding the shortest distance when a salesman visits a number of cities, provided that each city is visited exactly once and then returns to the initial city. TSP simulations for fruit distribution in Bogor city with each location having x and y coordinates as distances. The TSP used is a symmetrical TSP whose distance from city a to b has the same distance as city b to a. To solve and find solutions to problems using the Simulated Annealing (SA) Algorithm. The working principle is that at high temperatures metal liquid particles have a high energy level so it is relatively easy to move against other particles. Then as the temperature drops the particle slowly adjusts itself to form a configuration so that a stable state with a minimum energy level is obtained. This minimum energy is the shortest distance. Based on experiments that have been done using SA Algorithm on the TSP problem, the results show that the number of iterations that produce the optimal solution depends on the number of simulated locations. The more simulated location points, a large number of iterations are needed.
Performance analysis of Navigation AI on Commercial Game Engine: Autodesk Stingray and Unity3D Moch Fachri; Ali Khumaidi; Nur Hikmah; Nuke L Chusna
Jurnal Mantik Vol. 4 No. 1 (2020): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research establish crowd simulation on modern game engine such as Autodesk Stingray and Unity 3D. This paper explores the navigation system of both game engine. Furthermore we compare the navigation performance for each navigation system used by those engine: The gameware Navigation which is used in Stingray as its middleware for navigation AI, and Unity Navigation used in Unity3D. We simulate the crowd simulation using scenario of crossroad and narrow-passage. Experimental result demonstrates the navigation of hundreds of agents in densely populated environments.
Crowd Modelling and Navigation in Unity3D Game Engine: Crowd Modelling and Navigation in Unity3D Game Engine Mochammad Fachri; Avip Kurniawan; Ali Khumaidi
Jurnal Mantik Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.823.pp1185-1191

Abstract

In the curret era of building construction and development, it is very difficult to foresee the evacuation point of a building if disaster occure, especially for huge and tall buildings. For that purpose we make crowd modeling on the Unity3D game engine. We try to build a concept using evacuation scenarios in the Krisnadwipayana University, Faculty of Engineering building. By utilizing crowd modeling using Unity3D game engine, we will put all crowd agents together at the virtual environment of the target building at maximum density. In this study we using the prototype method to build the virtual area/building, and mapping the movement of the crowds in order to calculate the movement to be carried out in a form of visual crowds movement. The results of research conducted show that the final prototype that was made can accept 3D building models using Sketchup. From the data that has been made through the prototype there are 4498 agents who exit through the main door and 2068 agents use the back door of the target building, with the time needed to evacuate for 2 minutes and 16 seconds. The conclusion of this research is that it is able to present a crowd modeling form with integrated navigation on the game engine, and by using Unity3D for crowd modeling, it can help various groups that require testing in an area.
Development of Slum District Application in The City of Bekasi Based on Web Nur Hikmah; Nuke L Chusna; Ali Khumaidi
Jurnal Mantik Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.1015.pp1803-1807

Abstract

The Government of Bekasi City through The Department of Public Housing, Settlement Areas and Lands (DISPERKIMTAN) has a program to reduce slum areas with the City Without Slum Program (KOTAKU). District data collection is currently being carried out manually by the team and then the data is entered into the information system which can only be accessed internally. Existing data are not updated quickly so it is sometimes difficult to determine policies related to development assistance. Rukun Tetangga (RT) and Rukun Warga (RW) are part of the government structure closest to the community so that if data collection is carried out by them, the data will be fast and updated. This slum application development was developed using the SDLC model of waterfall method, while the stages include analysis, system design, system implementation, and system testing. Testing the application using a black box and the results are in accordance with the scenario and expectations.
Development of Drainage Status Prediction Model Based on Internet of Things and Long Short Term Memory Algorithm Ahmad Pahrul Rodji; Wargijono Utomo; Ali Khumaidi; Hudzaifah Al jihad
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The capacity of drainage can overflow due to inadequate conditions and high rainfall intensity. Several incidents in Bekasi City due to poor drainage resulted in inundation of water on the roads which resulted in damaged roads and flooding in residential areas. Several previous studies have discussed the evaluation of the drainage system using the analytical method hydrology in modeling water discharge. In most cases, the minimum capacity of the drainage canal is caused by the high intensity of rain, so the research focuses on the volume of drainage and the intensity of the rain. However, based on observations and interviews with the cleaning service, it turns out that many drainage channels are in a non-optimal condition, where there is a lot of garbage and sedimentation that hinders the flow of water when it rains. This study combines hydrological analysis modeling with drainage channel conditions whose real time data is obtained by using sensors through the internet of things (IoT). IoT devices have been able to send data well in the cloud, by combining rainfall data and then predictive modeling using RNN LSTM with training model parameters used are two layers and 20 cells with each layer given a Dropout layer with a probability of 10%. In the metric evaluation, four functions are used, namely mean squared error, Mean absolute, Nash-Sutcliffe Efficiency and Coefficient of Determination. The model has been able to see the occurrence of an increase or decrease in height and discharge. However, if you look at the results of metric calculations, the predictions generated by the model are not very good.
Prediction of Electricity Usage in The Food and Beverage Department Using Recurrent Neural Network Lukman Aditya; Wargijono Utomo; Ali Khumaidi; Rahmat Hidayat; Hudzaifah Al jihad
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The Food and Beverage (F&B) department is one of the sources of income for the company. F&B uses a variety of equipment and machines with large enough power consumption to support operations. F&B can be a disadvantage because of the wasteful use of electrical energy. This research designs and builds an Internet of Things (IoT) prototype that can monitor electricity usage in electrical equipment using sensors then from the data sent by the sensor and additional data predictions are made. The electrical equipment studied included walk-in chillers, blower wheels, exhaust fans, freezers, dishwashers, water heaters and under chillers. To build IoT devices, Arduino nano, AC Current Module, SIM 800L and humidity and temperature sensors are used. Prediction model built using RNN LSTM. IoT devices have succeeded in sending data well after cloud architecture. With 8 neurons in LSTM with lookback has the best performance. The error values ??for the test data are 51,085 and 18,886 for RMSE and MAE.
Development of The Application for Car Audio Parts Detection Damage Using Case Based Reasoning Method and Nearest Neighbor Algorithm Andika Saputra; Ali Khumaidi
Jurnal Teknik Informatika C.I.T Medicom Vol 13 No 1 (2021): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol13.2021.45.pp42-50

Abstract

PT. Denso Ten often receives car audio spare parts that are damaged due to shocks during the trip or sender's error. Damaged parts are collected and repaired by maintenance who has special skills manually. The limited number of maintenance operators and the frequent transfer of experts resulted in work delays due to insufficient spare parts. Spare parts repair work cannot be done by all employees because it requires special skills. The Case-based Reasoning approach and Nearest Neighbor algorithm are used to be developed for expert systems to support the detection of audio part damage so that it will speed up work and can be done by employees without special knowledge. The system can run and be used by users properly as needed and the results have good accuracy. The Case Base Reasoning method and the nearest neighbor algorithm work according to the rules and the calculation results are according to the expert's results.
Dissolved Oxygen Prediction of the Ciliwung River using Artificial Neural Networks, Support Vector Machine, and Streeter-Phelps Yonas Prima Arga Rumbyarso; Nuke L Chusna; Ali Khumaidi
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 10 No 3 (2022): Vol. 10, No. 3, December 2022
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2022.v10.i03.p06

Abstract

Evaluation of Ciliwung river water quality can be done by analyzing the distribution of dissolved oxygen (DO). The purpose of this research is to analyze the environmental parameters that affect the distribution of DO, by carrying out predictive modeling to estimate the distribution of DO in the Ciliwung River. The research data used primary data and secondary data, some of which were obtained from previous studies. The water quality parameters used are DO, temperature, biochemical oxygen demand, chemical oxygen demand, power of hydrogen, and turbidity. The dataset used has a missing value of 28.8%. To optimize the model results, preprocessing is carried out using a machine learning approach, namely comparing support vector machine (SVM), artificial neural networks (ANN), and linear regression. The three models were compared to predict DO, the results of performance evaluation of the SVM, ANN and Streeter-Phelps models had RMSE values of 0.110, 0.771, and 0.114.