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All Journal JUITA : Jurnal Informatika CogITo Smart Journal Jurnal Linguistik Komputasional Frontiers JOINTER : Journal of Informatics Engineering Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Abdimas : Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Cindy Pamela C Munaiseche
Universitas Negeri Manado
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mechanical Engineering Other

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Journal : JOINTER : Journal of Informatics Engineering

 1 
Sistem Informasi Beban Kerja dan Laporan Kinerja Dosen Berbasis Web Dwiputri R.A Pusung; Cindy P. Munaiseche; Olivia Kembuan
JOINTER : Journal of Informatics Engineering Vol 1 No 01 (2020): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

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

Abstract

Lecturer Workload (LW) and Lecturer Performance Report (LPR) is an illustration of the measurement of the "Tri Dharma Lecturer" in each semester. The filling of LW-LPR at Manado State University until the beginning of the even semester of the 2019-2020 school year still uses desktop-based applications that have not been integrated, so the assessment procedures cannot be carried out effectively and efficiently. This study aims to develop a web-based BKD-LKD filling and inspection application that can facilitate the input of workload and lecturer performance reports. The development of this system uses a research method namely the Prototype model which consists of the stages of listening to customers, building/repairing prototypes, testing customers. Testing this system uses the blackbox testing method.
Pemodelan Dan Simulasi Penyebaran Penyakit Covid-19 Dengan Menggunakan Model Cellular Automata Vira Chlaudia Makahinda; Gladly Caren Rorimpandey; CIndy Pamela Munaiseche
JOINTER : Journal of Informatics Engineering Vol 3 No 01 (2022): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v3i01.52

Abstract

The covid-19 is a virus that is currently plaguing the world by a highly unexpected form of infection. The covid-19 virus infection itself has grown remarkably fast not only in Indonesia but worldwide. Even its spread can be spread quickly and people are vulnerable to the virus. Based on data obtained from the spread of Sitaro health services, the number of covid19 cases in Sitaro district from April to June was 139 with 10 dead and 116 with a recovery from covid-19. In the case of covid19, there is a dire need for cooperation from all good people of the general public, health and government efforts to be together-together prevent further spread of covid-19 in our area. At current research the author USES the cellular model in simulation making. The cellular automata model was first introduced by Jhon Von Neuman and Stain Slaw Marchin Ulam in 1948 with the cellular space name that was a simple model for studying the behavior of a large complex system and learning biological processes. Cellular automata in this study was also used to predict the covid-19 spread spread in the coming month.
Analisis Kepuasan Pelanggan Menggunakan Metode Algoritma C4.5 Berdasarkan E-Survey Kejaksaan Negeri Minahasa Mohamad Alparizi Sahadan; Prof. Dr. Ing. Parabelem T. D. Rompas, MT; Cindy P. C. Munaiseche, ST, M. Eng
JOINTER : Journal of Informatics Engineering Vol 4 No 01 (2023): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v4i01.137

Abstract

Customers in the Minahasa State Prosecutor’s Office are the main thing in service. A customer supports experiencing various levels of satisfaction, namely if the service is not appropriate after being received, then the customer will feel dissatisfied. On the other hand, if the service is in line with expectations, the customer will feel satisfied that one day the customer will use the service. The calcutation method use is the C4.5 algorithm. The software development method used is Prototype. Data collection methods use are observation, literature and interview. Based on the first and second tests, customer satisfaction data on administrative, case, legal, and technical service with 80 training data and 20 testing data for the first test and 90 training data and 10 data testing data for the second test obtained an accuracy result of 90% for administrative service satisfaction, 90% accuracy for case service satisfaction, 90% accuracy for legal service satisfaction and 90% accuracy for technical service satisfaction. So that it can be satisfaction with service based on the e-survey of the minahasa state prosecutor’s office.
Analisis Performa Autoregressive Integrated Moving Average Model dan Deep Learning Long Short-Term Memory Model untuk Peramalan Data Cuaca Montolalu, Vithiaz; Munaiseche, Cindy; Krisnanda, Made
JOINTER : Journal of Informatics Engineering Vol 5 No 02 (2024): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v5i02.112

Abstract

Weather is an aspect that cannot be separated from all activities carried out by humans, so information about the weather is very important. To meet the need for this information, it is necessary to do forecasting. Each data has its own characteristics, and choosing the right forecasting method is very important. The Autoregressive Integrated Moving Average (ARIMA) method is one of the popular statistical methods used in forecasting time-series data. Long Short-Term Memory (LSTM) is a modern deep learning algorithm model that is most suitable for forecasting time-series data. In this study, an analysis was carried out to compare the traditional ARIMA method and the deep learning model, namely LSTM, in forecasting weather data in Manado city to see the best forecasting model that can be used. The results of this study indicate that in terms of the accuracy of the 18 tests performed, the LSTM forecasting model is superior to the ARIMA model as measured by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). In terms of computational time in making forecasting models for 6 weather data attributes, the LSTM model is faster than the ARIMA model.
Co-Authors Alfrina Mewengkang Audy Aldrin Kenap Cipta Ramadhani Dalle, Asnir Dwiputri R.A Pusung Gladly Caren Rorimpandey Hingide, Meigi Nivgi Hiskia Kamang Manggopa Julio Rayen Solang Laode Muhamad Tajidun Lumanauw, Renaldo Made Krisnanda Meigi Nivgi Hingide Mohamad Alparizi Sahadan Montolalu, Vithiaz Nancy Sylvia Bawiling Olivia Kembuan parabelem tinno dolf rompas Pusung, Dwiputri Rosdiana Angela Solang, Julio Rayen Trudi Komansilan Vira Chlaudia Makahinda Vivi Peggie Rantung