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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi METIK JURNAL Journal of Information Technology and Cyber Security
Suyono, Ayu Adelina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering

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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.
Analisis Sentimen Haramnya Musik Secara Umum Mengunakan Metode KNN Rahmat Saudi Al Fathir; Thami Rusdi Agus; Ayu Adelina Suyono; Fardiansyah Ibrahim
METIK JURNAL Vol 5 No 2 (2021): METIK Jurnal
Publisher : LP3M Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v5i2.284

Abstract

Penelitian ini membahas tentang penerapan algoritma K-Nearest Network dalam kasus analisis sentimen. Analisis sentimen dilakukan terhadap komentar dalam video yang membahas tentang haramnya musik. Sumber data diambil ceramah Ustadz Dr. Syafiq Riza Basalamah, M.A. dengan judul Hukum Musik dalam Islam Beserta Dalilnya, Hukum Musik dalam Islam LENGKAP: Musik HALAL atau HARAM, dan Hadits Haramnya Musik Dhaif. Data komentar yang digunakan sejumlah 2114 data berbahasa Indonesia. Komentar dibagi menjadi 3 kelas, diantaranya Menerima, Tidak Menerima, dan Bingung. Hasil pengujian menunjukkan nilai akurasi yang relatif rendah, yaitu pada tingkat 65%. Hal ini diakibatkan karena kurangnya jumlah data yang disertakan dalam pengujian.
Prediksi Indeks Harga Konsumen Komoditas Makanan di Kota Surabaya menggunakan Support Vector Regression Ayu Adelina Suyono; Kusrini Kusrini; Muhammad Rudyanto Arief
METIK JURNAL Vol 6 No 1 (2022): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v6i1.339

Abstract

In data mining, predictions are known to find knowledge about what will happen in the future. Predictions are usually made on time-series data. The Consumer Price Index (CPI) is an index value derived from daily consumer price data. The results of the CPI calculation are derived from observations of commodity prices at the household consumer level, which are carried out routinely on a daily, weekly, bi-weekly, and monthly basis. CPI prediction can be done using a data mining algorithm, namely Support Vector Regression (SVR). SVR is part of the Support Vector Machine algorithm that functions to solve regression cases. SVR is a reliable algorithm in the case of regression because it can handle data overfitting well. The data used as input in this paper comes from 34 food commodity prices, and the output data is obtained from the CPI value data. The food commodity price data used is from Surabaya City. The data period used is from 2014-2020. The results of the implementation of SVR with 4 kernels show that the Polynomial kernel has the best error rate with a MAPE value of 4.31%.
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.
Co-Authors Erliyah Nurul Jannah, Erliyah Nurul Fardiansyah Ibrahim Indianiati, Ulfiatin Nur Kusrini Kusrini Muhammad Rudyanto Arief Rahmat Saudi Al Fathir Rizki, Enes Maulia Siti Hamidah Thami Rusdi Agus