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Identifikasi Faktor Kenaikan Harga Komoditas Pasar Melalui Agregasi Berita Online (Studi Kasus: Jawa Timur) Rengga Asmara; Ferry Astika Saputra; Muhammad Rizal Fauzy
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 11 No. 1 (2020): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Abstrak Penelitian ini bertujuan untuk membuat sistem monitoring harga komoditas yang mampu mengidentifikasi permasalahan penyebab kenaikan harga komoditas melalui agregasi berita online. Sistem ini memiliki keluaran utama, yaitu monitoring fluktuasi harga pasar dan identifikasi penyebab kasus kenaikan harga komoditas menggunakan data harga komoditas harian yang dikumpulkan dari seluruh pasar induk di Jawa Timur pada Desember 2018 - Mei 2019 yang disediakan oleh SISKAPERBAPO. Untuk mendapatkan hasil identifikasi, sistem melakukan beberapa tahap. Pertama, sistem melakukan labelling terhadap harga pasar yang mengalami kenaikan tidak normal. Selanjutnya, sistem mencari berita terkait kenaikan harga tersebut dan melakukan information retrieval berdasarkan klasifikasi teks terhadap 5W1H pada label why. Hasil dari penelitian ini yaitu, eksperimen pada identifikasi faktor kenaikan harga komoditas menunjukkan bahwa sebesar 1.91% berita memiliki kesesuaian berdasarkan tanggal berita, akurasi berdasarkan relevansi berita dan kasus sebesar 70.49%, dan ekstraksi informasi untuk identifikasi faktor menghasilkan sebesar 39.87% berita relevan menunjukkan hasil identifikasi faktor kenaikan harga komoditas. Kata kunci: Monitoring System, Tim Pengendalian Inflasi Daerah (TPID), Information Retreival, Klasifikasi 5W1H Abstract This research aims to create a commodity price monitoring system that is able to identify the problems causing the increase in commodity prices through online news aggregation.. This system has main outputs, namely monitoring market price fluctuations and identifying causes of cases of rising commodity prices from online news aggregations, which used daily commodity price data collected from all wholesale markets in East Java in December 2018 - May 2019 provided by SISKAPERBAPO. To get the results of identification, the system performs several stages of processing. First, the system of labeling market prices has increased. Furthermore, the system looks for news related to the price increases and conducts information retrieval based on the classification of text against 5W1H on the label why. The results of this research, namely, the experiment on identifying commodity price increase factors shows that 1.91% of news has conformity based on news date, accuracy based on news and case relevance of 70.49%, and information extraction to identify generating factors of 39.87% relevant news shows results identification of factors in rising prices. Keywords: Monitoring System, Tim Pengendalian Inflasi Daerah (TPID), Information Retreival, 5W1H Classification

Water Quality Level for Shrimp Pond at Probolinggo Area Based on Fuzzy Classification System Fithrotul Irda Amaliah; Agus Indra Gunawan; Taufiqurrahman Taufiqurrahman; Bima Sena Bayu Dewantara; Ferry Astika Saputra
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Since several years ago, vaname shrimp (Litopenaeus vannamei) has been extensively cultivated in Indonesia because it has good business opportunities. In aquaculture activities, water quality is an important factor that dramatically impacts the survival and quality of shrimp in the pond. Therefore, information of water quality must be known by the farmer for obtaining a satisfactory harvest. This study aims to develop a water quality monitoring system based on information of temperature, pH, salinity, and dissolved oxygen. The data from sensors are sent to the cloud utilizing Internet of Things (IoT) technology and then classified by a fuzzy logic system. In order to help farmers easily know the water quality of their shrimp pond, four sensor data including the result of classification from fuzzy logic are sent to the phone. After a trial of the system, 100% of the data are successfully sent to the cloud (google spreadsheet). The system also successfully classified the level of water quality as the expectation of the farmer. With this system, it is hoped that it can assist farmers in monitoring the water quality of shrimp pond to improve the quality and quantity of shrimp.

Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil Ricky Afiful Maula; Agus Indra Gunawan; Bima Sena Bayu Dewantara; M. Udin Harun Al Rasyid; Setiawardhana Setiawardhana; Ferry Astika Saputra; Junaedi Ispianto
Jurnal Rekayasa Elektrika Vol 18, No 3 (2022)
Publisher : Universitas Syiah Kuala

Shrimp cultivation is strongly influenced by pond water quality conditions. Farmers must know the appropriate action in regulating water quality that is suitable for shrimp survival. The state of water quality can be understood by measuring pond parameters using various sensors. Installing sensors equipped with artificial intelligence modules to inform water quality conditions is the right action. However, the sensor cannot be separated from errors, so it results in not being able to get data or missing data. In this case, the approach of 5 parameters of pond water quality from 13 available parameters is carried out. This paper proposes a technique to obtain lost data caused by sensor error and looks for the best model. A simple approach can be taken, such as the Handling Missing Value (HMV), which is commonly used, namely the mean, with the K-Nearest Neighbors (KNN) classifier optimized using a grid search. However, the accuracy of this technique is still low, reaching 0.739 at 20-fold cross-validation. Calculations were carried out with other methods to further improve the prediction accuracy. It was found that Linear Regression (LR) can increase accuracy up to 0.757, which outperforms different approaches such as the statistical approach to mean 0.739, mode 0.716, median 0.734, and regression approach KNN 0.742, Lasso 0.751, Passive Aggressive Regressor (PAR) 0.737, Support Vector Regression (SVR) 0.739, Kernel Ridge (KR) 0.731, and Stochastic Gradient Descent (SGD) 0.734.

Water Quality Level for Shrimp Pond at Probolinggo Area Based on Fuzzy Classification System Fithrotul Irda Amaliah; Agus Indra Gunawan; Taufiqurrahman Taufiqurrahman; Bima Sena Bayu Dewantara; Ferry Astika Saputra
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i1.28631

Since several years ago, vaname shrimp (Litopenaeus vannamei) has been extensively cultivated in Indonesia because it has good business opportunities. In aquaculture activities, water quality is an important factor that dramatically impacts the survival and quality of shrimp in the pond. Therefore, information of water quality must be known by the farmer for obtaining a satisfactory harvest. This study aims to develop a water quality monitoring system based on information of temperature, pH, salinity, and dissolved oxygen. The data from sensors are sent to the cloud utilizing Internet of Things (IoT) technology and then classified by a fuzzy logic system. In order to help farmers easily know the water quality of their shrimp pond, four sensor data including the result of classification from fuzzy logic are sent to the phone. After a trial of the system, 100% of the data are successfully sent to the cloud (google spreadsheet). The system also successfully classified the level of water quality as the expectation of the farmer. With this system, it is hoped that it can assist farmers in monitoring the water quality of shrimp pond to improve the quality and quantity of shrimp.

Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil Ricky Afiful Maula; Agus Indra Gunawan; Bima Sena Bayu Dewantara; M. Udin Harun Al Rasyid; Setiawardhana Setiawardhana; Ferry Astika Saputra; Junaedi Ispianto
Jurnal Rekayasa Elektrika Vol 18, No 3 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i3.25903

Shrimp cultivation is strongly influenced by pond water quality conditions. Farmers must know the appropriate action in regulating water quality that is suitable for shrimp survival. The state of water quality can be understood by measuring pond parameters using various sensors. Installing sensors equipped with artificial intelligence modules to inform water quality conditions is the right action. However, the sensor cannot be separated from errors, so it results in not being able to get data or missing data. In this case, the approach of 5 parameters of pond water quality from 13 available parameters is carried out. This paper proposes a technique to obtain lost data caused by sensor error and looks for the best model. A simple approach can be taken, such as the Handling Missing Value (HMV), which is commonly used, namely the mean, with the K-Nearest Neighbors (KNN) classifier optimized using a grid search. However, the accuracy of this technique is still low, reaching 0.739 at 20-fold cross-validation. Calculations were carried out with other methods to further improve the prediction accuracy. It was found that Linear Regression (LR) can increase accuracy up to 0.757, which outperforms different approaches such as the statistical approach to mean 0.739, mode 0.716, median 0.734, and regression approach KNN 0.742, Lasso 0.751, Passive Aggressive Regressor (PAR) 0.737, Support Vector Regression (SVR) 0.739, Kernel Ridge (KR) 0.731, and Stochastic Gradient Descent (SGD) 0.734.

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