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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Konvergensi Research In Management and Accounting (RIMA) Journal of Information System,Graphics, Hospitality and Technology Jurnal Informatika dan Sistem Informasi Jurnal Teknik Informatika (JUTIF) Teknika Jurnal AbdiMas Nusa Mandiri Insyst : Journal of Intelligent System and Computation JUSTIN (Jurnal Sistem dan Teknologi Informasi) Joutica : Journal of Informatic Unisla Jurnal Nasional Teknik Elektro dan Teknologi Informasi Reflection Journal Jurnal Sistem Cerdas dan Rekayasa Jurnal Sistem Informasi Research In Management and Accounting (RIMA)
Devi Dwi Purwanto
Institut Sains Dan Teknologi Terpadu Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Physics Social Sciences Other

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Journal : Insyst : Journal of Intelligent System and Computation

 1 
Klasifikasi Kategori Hasil Perhitungan Indeks Standar Pencemaran Udara dengan Gausian Naïve Bayes (Studi Kasus: ISPU DKI Jakarta 2020) Purwanto, Devi Dwi; Honggara, Eric Sugiharto
Intelligent System and Computation Vol 4 No 2 (2022): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v4i2.259

Abstract

Pencemaran udara adalah masalah yang membahayakan manusia terutama untuk sistem pernafasan. Saat ini pencemaran udara selalu terjadi akibat beberapa hal seperti asap kendaraan, pembangkit listrik dan lainnya. Salah satu tempat di mana pencemaran udara terjadi adalah di kota besar di mana banyak orang berkumpul. Salah satu tempat yang menjadi perhatian adalah stasiun yang berada di daerah khusus ibukota jakarta. Stasiun adalah tempat di mana banyak orang berkumpul dan menunggu untuk melakukan perjalanan. Maka dari itu dinas lingkungan hidup DKI Jakarta membuka data pencemaran udara yang terjadi di stasiun agar dapat digunakan oleh masyarakat untuk diolah. Data tersebut akan dilakukan preprocessing yaitu penanganan missing value, normalisasi data, dan menggunakan one hot encoding. Data tersebut kemudian akan diklasifikasi dengan menggunakan algoritma Gausian Naïve Bayes. Setelah memperoleh hasil dari klasifikasi dapat disimpulkan bahwa atribut max dan critical yang berada dalam dataset tidak memiliki pengaruh terhadap hasil klasifikasi kategori ISPU. Atribut-atribut dari data yang berpengaruh terhadap klasifikasi kategori ISPU adalah PM10, SO2, CO, O3, dan NO2. Dengan menggunakan 5 atribut dan gausian naïve bayes, sistem dapat memberikan klasifikasi dengan akurasi sebesar 91,16% dan memiliki error rate sebesar 8,84%. Sedangkan nilai Weighted Average Recall 93,36%, Weighted Average Precision 93,92% , dan Weighted Average F1-Score sebesar 93,68%.
Comparison of Premium Rice Price Prediction in East Java with ARIMA and LSTM (Case Study: National Food Agency Data) Purwanto, Devi Dwi; Sitepu, Rasional; Honggara, Eric Sugiharto
Intelligent System and Computation Vol 6 No 2 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i2.407

Abstract

Rice price prediction plays a crucial role in maintaining economic stability and food security, especially in East Java, one of Indonesia's major rice production centers. This study aims to forecast premium rice prices in East Java using the ARIMA (AutoRegressive Integrated Moving Average) method. The data utilized in this research comprises premium rice prices obtained from the National Food Agency over the period from March 15, 2021, to October 17, 2024. The analysis process begins with data exploration to identify trends and seasonal patterns in the rice price data. Subsequently, the data is analyzed using ARIMA and LSTM methods, both recognized for their effectiveness in time-series forecasting. The ARIMA(1,1,1) model was selected due to its capability to capture price dynamics through its autoregressive, integrated, and moving average components, making it well-suited for linear data with minimal seasonal variation. LSTM was employed as a comparative model because it is a subset of Machine Learning that integrates computational models and neural network algorithms, offering potential improvements in prediction accuracy. The LSTM model used for prediction consists of four layers, each with 50 neurons, dropout rates of 20% and 30%, and a single output layer representing the predicted price. The results indicate the ARIMA model provides highly accurate price estimates with a Mean Absolute Percentage Error (MAPE) of 0.485%, whereas the LSTM model achieves a MAPE of 1.95%. These findings serve as a reference for policymakers and food industry stakeholders in formulating strategic measures to stabilize rice prices in East Java.
Co-Authors . Gunawan . Gunawan . Gunawan . Gunawan Aditya Dwi Aryanto Agustinus Bimo Gumelar Aji Prasetya Wibawa Budianto, Herman Endang Setyati Eric Sugiharto Honggara Gabeler, Robertus Geraldyn Alexandro Hadikusuma, Jeremiah Irawan Hakkun Elmunsyah Hartarto Junaedi Herman Budianto Herman Thuan To Saurik Hongary, Kevin - Ikhsan, Teguh Pribadi Indra Maryati Jimmy Tegarianto Joan Santoso Kartika Salim, Shierly Kevin - Hongary Kristian, Yosi Limantoro, Stephen Ekaputra Mochammad Faid Nathaniel Tjoa Novi Duwi Setyorini Setya Ardhi Sitepu, Rasional Siti Sendari Slamet Winardi Stefanie Angelina Gunarto Stephen Ekaputra Limantoro Subandoro, Philipus Suryo Suhatati Tjandra Tristianti, Berliana Az Zahra Yosi Kristian Yunan Kholilul Fatah