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ANALISIS PEMBERIAN NUTRISI MENGGUNAKAN METODE FUZZY LOGIC STUDI KASUS TANAMAN CABAI Devin Viondra Sihar Matondang; Delmanto Saogo; Rizki Samuel Putra F. Sianturi; Sopian Dapit; Ertina Sabarita Barus
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.929

This research addresses the challenges faced by chili farmers in predicting harvest outcomes and providing nutrition according to plant needs. The Fuzzy Logic Mamdani method is proposed as a decision support system to determine the optimal nutrient application volume based on soil conditions, temperature, and acidity level (pH). By synthesizing fuzzy logic, this study aims to assist chili farmers in optimizing harvest results through appropriate nutrient application. The research methodology involves literature review, observation, and data collection to form a dataset with variables such as irrigation volume, soil moisture, temperature, and soil pH. The research results include the formation of fuzzy sets, application of implication functions, rule composition, defuzzification, and irrigation volume calculations. Simulations using the Fuzzy Logic Toolbox show that the Fuzzy Logic Mamdani method can provide predictions of nutrient application volumes that align with chili plant conditions. In testing, when the input variable of water content is 35.6 at a temperature of 30.8 with a soil pH of 3.7, the system recommends an irrigation volume of 204, categorized as high. The findings of this research can serve as a predictive tool to help chili farmers determine the optimal nutrient application volume based on environmental conditions.

ANALISIS PREDIKSI HASIL PRODUKSI TANAMAN CABAI MENGGUNAKAN METODE MULTI LINIER REGRESI Sahputra, Sahputra; Sembiring, Delima Chrismas; Sipayung, Ivan Hasadaon; Barus, Ertina Sabarita
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1512

This study aims to predict the yield of chili plants in Indonesia using multiple linear regression method. In this study, the variables analyzed include irrigation volume, temperature, soil moisture, soil pH, and plant growth parameters such as stems, branches, and leaves. Data were collected from 100 chili plant samples planted in Jatikusuma Village, Deli Serdang Regency, for 63 days. The method used for the analysis is multiple linear regression, which is applied to produce a prediction model of harvest yield. Multi linear regression method is used to perform forecasting with the development of the dependent variable (Y), namely the amount of production with independent variables consisting of x1 = plant growth rate, x2 = moisture, x3 = temperature, x4 = volume, x5 = soil pH, x6 = stem, x7 = branch, x8 = leaf. The results of the prediction analysis in this study obtained the intercept coefficient value is 153.94 from the total data of 100 samples, resulting in the level of fit of the multi linear regression model with an R2 score of 1.00 which shows the level of accuracy in a prediction of these results is very good.

IMPLEMENTASI METODE RANDOM FOREST UNTUK MEMPREDIKSI PENJUALAN PRODUK Barus, Ertina Sabarita; Darmanto, Darmanto
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1510

This study aims to predict product sales at CV Pelumas Murni Keluarga using the Random Forest method to overcome sales fluctuations that impact stock management and production planning. Uncertainty in sales forecasting can cause excess or shortage of stock, thus hampering the company's growth. This method was chosen because of its advantages in handling complex data and producing accurate predictions. The study was conducted quantitatively through observation and collection of automotive lubricant sales data from January to June 2023. Data was analyzed using the Google Colab application to implement the Random Forest model. The process involves data preprocessing, model building, and evaluation using out-of-bag data. The results of the study show that the Random Forest method is able to significantly increase the accuracy of sales predictions, providing a stronger foundation in developing sales strategies and inventory management. Thus, this study is expected to help CV Pelumas Murni Keluarga in optimizing operational efficiency and increasing profitability.

Pemetaan Objek Retribusi Menggunakan GIS Untuk Meminimalisir Pungli Ertina Sabarita barus; Bersama Sinuraya; Jenni Veronika Ginting; Niskarto Zendrato; Diana Alemin Barus
Prosiding Seminar SeNTIK Vol. 2 No. 1 (2018): Prosiding SeNTIK 2018
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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

Pemetaan Objek Retribusi Menggunakan GIS Untuk Meminimalisir Pungli

COMPARATIVE ANALYSIS OF STROKE CLASSIFICATION USING THE K-NEAREST NEIGHBOR DECISION TREE, AND MULTILAYER PERCEPTRON METHODS Barus, Ertina Sabarita; Halim, Jenny Evans; Yessica, Sally
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4083

Stroke has become a serious health problem; the main cause of stroke is usually a blood clot in the arteries that supply blood to the brain. Strokes can also be caused by bleeding when blood vessels burst and blood leaks into the brain. In one year, about 12.2 million people will have their first stroke, and 6.5 million people will die from a stroke. More than 110 million people worldwide have had a stroke. Handling that is done quickly can minimize the level of brain damage and the potential adverse effects. Therefore, it is very important to predict whether a patient has the potential to experience a stroke. The K-Nearest Neighbor, Decision Tree, and Multilayer Perceptron algorithms are applied as a classification method to identify symptoms in patients and achieve an optimal accuracy level. The results of making the three algorithms are quite good, where K-Nearest Neighbor (K-NN) has an accuracy value of 93.84%, Decision Tree is 93.97%, and Multilayer Perceptron (MLP) is 93.91%. The best accuracy value is the Decision Tree algorithm with an accuracy difference of no more than 0.10% with the two algorithms used.

Analysis of Anthracnose Disease in Curly Chilli Using Fuzzy Logic Method simangunsong, Esterika; Situmeang, Johan Medi; Aikel; Barus, Ertina Sabarita
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/xtkp9c52

Curly chilli (Capsicum annuum L.) is one of the horticultural products that has a high economic value and is often consumed by the people of Indonesia, both as a flavour enhancer for dishes and as a source of nutrition. However, until now, the production of chilli peppers has not been able to meet demand, one of which is caused by anthracnose disease that attacks plants through fungi of the genus Colletotrichum, potentially causing yield losses of 50 to 90%. Until now, there have not been many disease risk prediction systems that consider environmental variables adaptively. This research aims to develop an anthracnose disease risk prediction system based on the Mamdani fuzzy logic method that is able to handle the uncertainty of environmental data such as temperature, humidity, and soil pH. Data are obtained from trusted literature sources and have undergone a validation process before being used in modelling. The system was developed using MATLAB because it supports various features in the implementation of fuzzy logic. Simulation results show high consistency between manual calculations and software results, indicating that the system has a good level of accuracy and potential to be applied in agricultural management.

Forecasting Red Chilli Plant Growth using Time Series Method With Long Short-Term Memory Model Aritonang, Lastiur; Aryowindo, Brita; Syarif, Ridho; Barus, Ertina Sabarita
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/24mwkh42

The growth of red chilli plants is a horticultural commodity whose growth is highly determined by environmental elements, as a result, it is very crucial to make predictions to help more effective agricultural planning. This study aims to examine the ability of the Long Short-Term Memory (LSTM) model in predicting the growth of red chilli plants (Capsicum annuum L.) according to 4 main parameters, namely stems, branches, leaves, and grains. The data used are red chilli plant growth data obtained from plantations located in Deli Serdang Regency, precisely in Namorambe District, namely Jatikusuma Village, over a period of 63 days and analyzed using the time collection method. The example provides high prediction accuracy for stem parameters (R² = 0.9796), branches (R² = 0.9618), and leaves (R² = 0.9489), but slightly low in fruit (R² = 0.8807) due to hyperbolic fluctuations. The consequences show the potential of LSTM in helping red chilli cultivation through better planning, green aid control, and early detection of growth anomalies. This study also demonstrates an integrative approach to four plant growth parameters using a single LSTM instance.

Implementasi Fuzzy Logic untuk Menentukan Kelayakan Pembangunan Infrastruktur: Implementasi Fuzzy Logic untuk Menentukan Kelayakan Pembangunan Infrastruktur Barus, Ertina Sabarita; Zendrato, Niskarto
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 2 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/jtiust.v6i2.1721

A computer simulation application system that can analyze the benefits of developing an infrastructure development project in a sub-district and simulated in the fuzzy toolbox application Matlab 7.9.2 In analyzing the benefits of infrastructure development, several economic rules and feasibility studies for infrastructure development are used, namely aspects of benefits, aspects of effectiveness and aspects of efficiency . These rules are applied to the results of the benefit data when infrastructure development is carried out in the first year, then the results of the benefit data are processed using Mamdani fuzzy logic reasoning which consists of 2 inference processes. In processing fuzzy input data, it produces output from the inference process which is then classified into 5 eligibility conditions, namely, low, normal, high, very high and not feasible, which are used as a means of supporting infrastructure development decisions in an area

Implementasi Fuzzy Logic untuk Menentukan Kelayakan Pembangunan Infrastruktur: Implementasi Fuzzy Logic untuk Menentukan Kelayakan Pembangunan Infrastruktur Barus, Ertina Sabarita; Zendrato, Niskarto
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 2 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

A computer simulation application system that can analyze the benefits of developing an infrastructure development project in a sub-district and simulated in the fuzzy toolbox application Matlab 7.9.2 In analyzing the benefits of infrastructure development, several economic rules and feasibility studies for infrastructure development are used, namely aspects of benefits, aspects of effectiveness and aspects of efficiency . These rules are applied to the results of the benefit data when infrastructure development is carried out in the first year, then the results of the benefit data are processed using Mamdani fuzzy logic reasoning which consists of 2 inference processes. In processing fuzzy input data, it produces output from the inference process which is then classified into 5 eligibility conditions, namely, low, normal, high, very high and not feasible, which are used as a means of supporting infrastructure development decisions in an area

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