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Analisis Aplikasi Integrated Postal Operations System (IPOS) Pada PT. Pos Indonesia (Persero) KPRK Jombang Menggunakan Metode PIECES Suyono, Ayu Adelina; Indianiati, Ulfiatin Nur; Rizki, Enes Maulia; Hamidah, Siti; Jannah, Erliyah Nurul
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 2, No 1 (2016): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v2i1.442

Abstract

PT. Pos Indonesia (Persero) Kantor Pemeriksa (KPRK) Jombang atau Kantor Pos Jombang merupakan Badan Usaha Milik Negara (BUMN) yang bergerak dalam bidang jasa pengiriman. Dalam proses bisnisnya, Kantor Pos Jombang menggunakan sebuah aplikasi bernama Integrated Postal Operations System (IPOS). Dalam artikel ini, penulis melakukan analisis pada Aplikasi IPOS untuk mengetahui penerapannya pada transaksi pengiriman surat dan barang di Loket Retail Kantor Pos Jombang dan mengetahui aspek PIECES (Performance, Information/Data, Economic, Control/Security, Efficiency, dan Service) dari Aplikasi IPOS. Metode pengumpulan data yang digunakan penulis adalah wawancara, observasi, dan studi pustaka. Dari hasil analisis yang telah dilakukan, dapat disimpulkan bahwa Aplikasi IPOS sangat mendukung Kantor Pos Jombang dalam transaksi pengiriman surat dan barang. Aplikasi IPOS dapat mempercepat dan mempermudah proses transaksi pengiriman, serta dapat memberikan informasi yang akurat, tepat waktu, dan relevan.Kata kunci: Proses Transaksi Pengiriman, Aplikasi IPOS, Metode PIECES. PT. Pos Indonesia (Persero) Kantor Pemeriksa (KPRK) Jombang or Jombang Post Office was the State Owned Enterprises (SOEs) which is engaged in delivery services. In its business processes, Jombang Post Office uses an application called Integrated Postal Operations System (IPOS). In this paper, authors analyzed IPOS to observe the PIECES (Performance, Information/Data, Economic, Control/Security, Efficiency, and Service) aspects of IPOS in the task of mail and goods delivery transaction in Retail Locket of Post Office Jombang. The, data collection methods used by the author are interview, observation, and literature study. From the analysis that has been done, it can be concluded that IPOS strongly supports Jombang Post Office in transaction of mail and goods delivery. IPOS can speed up and simplify the transaction of mail and goods delivery, and it can provide information that is accurate, timely, and relevant. Keywords: Transaction of Mail and Goods Delivery, IPOS, PIECES Methods.
A Comparison of Polynomial Regression and Support Vector Regression for Predicting the Consumer Price Index Based on Food Commodity Prices in East Java, Indonesia Suyono, Ayu Adelina
Journal of Information Technology and Cyber Security Vol. 3 No. 2 (2025): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.12353

Abstract

Food price fluctuations occur almost daily and directly affect purchasing power as well as the stability of regional and national economies. As one of the largest provinces in Indonesia, East Java, which significantly contributes to national GDP, has diverse economic structures and highly sensitive to price changes. Given this situation, government needs more accurate prediction methods to monitor Consumer Price Index (CPI) movement as a basis for establishing more appropriate economic strategy and policy. This study aims to compare the performance of Polynomial Regression (PR) and Support Vector Regression (SVR) in predicting CPI using food price data from SISKAPERBAPO for the 2014 - 2020 period, covering regencies and cities in East Java. To ensure the quality of the analysis, missing values were removed. A Pearson’s r correlation analysis was then conducted to assess the relationships between food prices and CPI. The model obtained was then evaluated using mean squared error (MSE), root mean square error (RMSE), Mean absolute percentage error (MAPE), and computation time. The results shows that third order PR achieved higher accuracy with MAPE of 0.3% (training) and 3.4% (testing), while SVR performed lower with MAPE of 5.9% (training) and 6.0% (testing). In addition, PR was more computationally efficient than SVR. These findings underscore PR as a more reliable method for predicting CPI using complex regional food data.