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H-WEMA: A New Approach of Double Exponential Smoothing Method

TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

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Abstract

A popular smoothing technique commonly used in time series analysis is double exponential smoothing. Basically, it’s an improvement of simple exponential smoothing which does the exponential filter process twice. Many researchers had developed the technique, hence Brown’s double exponential smoothing and Holt’s double exponential smoothing. Here, we introduce a new approach of double exponential smoothing, called H-WEMA, which combines the calculation of weighting factor in weighted moving average with Holt’s double exponential smoothing method. The proposed method will then be tested on Jakarta Stock Exchange (JKSE) composite index data. The accuracy and robustness level of the proposed method will then be examined by using mean square error and mean absolute percentage error criteria, and be compared to other conventional methods.

Brown’s Weighted Exponential Moving Average Implementation in Forex Forecasting

TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

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Abstract

In 2016, a time series forecasting technique which combined the weighting factor calculation formula found in weighted moving average with Brown’s double exponential smoothing procedures had been introduced. The technique is known as Brown’s weighted exponential moving average (B-WEMA), as a new variant of double exponential smoothing method which does the exponential filter processes twice. In this research, we will try to implement the new method to forecast some foreign exchange, or known as forex data, including EUR/USD, AUD/USD, GBP/USD, USD/JPY, and EUR/JPY data. The time series data forecasting results using B-WEMA then be compared with other conventional and hybrid moving average methods, such as weighted moving average (WMA), exponential moving average (EMA), and Brown’s double exponential smoothing (B-DES). The comparison results show that B-WEMA has a better accuracy level than other forecasting methods used in this research.

Sistem Pendukung Keputusan Pemilihan Program Studio di Universitas dengan Alogritma C4.5 (Studi Kasus: Universitas Multimedia Nusantara)

Teknik dan Ilmu Komputer Vol. 06 No. 23 Juli-September 2017
Publisher : Teknik dan Ilmu Komputer

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Abstract

AbstrakLebih dari setengah dari enam puluh persen mahasiswa baru yang telah memilih jurusan beralih dari jurusan dipilih sebelumnya ke jurusan yang baru. Hal ini terjadi karena kurangnya informasi mengenai jurusan yang tersedia di universitas dan ketidaksesuaian antara jurusan sebelumnya dengan minat dan bakat mahasiswa tersebut. Keputusan seorang mahasiswa dalam memilih jurusan sering dipengaruhi oleh orang tua, kerabat, dan teman-teman. Akibatnya, mahasiswa akan memilih jurusan yang tidak sesuai dengan minat atau bakat mereka. Hal ini akan mengakibatkan lingkungan belajar yang tidak kondusif yang pada gilirannya akan mengakibatkan penurunan kualitas sumber daya manusia yang dihasilkan karena lingkungan belajar yang tidak mendukung. Berdasarkan permasalahan tersebut, sistem pendukung keputusan berbasis desktop dibangun untuk membantu mahasiswa memilih jurusan yang relevan dengan minat atau bakat mereka. Sistem ini akan membantu mahasiswa memilih jurusan dengan menerapkan algoritma pohon keputusan, yang  disebut C4.5. Faktor yang sedang dipertimbangkan untuk sistem dalam membuat keputusan adalah nilai studi calon mahasiswa. Setelah beberapa percobaan yang dirancang untuk menguji akurasi sistem menggunakan Confusion Matrix dan Cross Validation, akurasi maksimum 95% tercatat sebagai hasil terbaik dari sistem dengan menggunakan Confusion Matrix.Kata kunci: sistem pendukung keputusan, jurusan perguruan tinggi, algoritma C4.5, Confusion Matrix, Cross ValidationAbstractOver half of sixty percent of new college students who had chosen their major switched their major. This happened due to information about the available major at the university was not clearly informed and the students previous major was irrelevant to their interest and talent. A college students decision in choosing a major was often influenced by parents, relatives, and friends. As a result, students would choose a major that was irrelevant to their interest or talent. This would result in an unconducive learning environment which would lead to degradation of the human resource quality. Based on the problem aforementioned, a desktop-based decision support system was built to help students choose a major relevant to their interest or talent. The system would help student choose their major by implementing a decision tree algorithm called C4.5. The factor considered for the system to decide was the students school grades. After several experiments designed to test the systems accuracy using Confusion Matrix and Cross Validation, a maximum accuracy of 95% was recorded as the best result of the system by using Confusion Matrix.Keywords: decision support system, college’s major selection, C4.5 algorithms, confusion matrix, cross validation

SISTEM REKOMENDASI LOWONGAN KERJA DENGAN GLASSDOOR API DAN METODE SIMPLE ADDITIVE WEIGHTING

Teknik dan Ilmu Komputer VOL. 7 NO. 26 APRIL-JUNI 2018
Publisher : Teknik dan Ilmu Komputer

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Abstract

Sistem rekomendasi adalah aplikasi yang memberikan saran alternatif kepada pengguna. Salah satu hal yang bisa dijadikan objek dalam sistem rekomendasi adalah lowongan kerja. Dalam memilih pekerjaan, ada banyak faktor untuk menentukan kesesuaian lowongan pekerjaan yang relevan dengan kriteria pengguna. Berdasarkan penjelasan di atas, aplikasi Job Carrier berbasis web dibangun untuk memberikan rekomendasi lowongan pekerjaan menggunakan algoritma SAW yang akan melakukan multiplikasi kriteria bobot dan data. Desain dan pengembangan aplikasi Job Carrier berbasis web ini menggunakan framework CodeIgniter, database MySQL, dan PHP, HTML, CSS, dan bahasa pemrograman Javascript. Pengujian terhadap pengguna telah dilakukan pada aplikasi Job Carrier dan diperoleh persentase keberhasilan sebesar 82,875%. Hasil kuesioner ini telah diperiksa dengan menggunakan Alpha Cronbach dan diperoleh nilai 0,7368 diperoleh. Hal ini menunjukkan bahwa hasilnya bisa dipercaya.Kata kunci: Kriteria, Job Vacancy, Sistem Rekomendasi, SAW, Bobot

AKU: A Web Based E-Complaint Application with Automated Complaints Classification

TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

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Abstract

In university’s terms, students’ complaints about the services provided are important things to note because if it’s not handled properly, it will lead to the higher students transfer. In Universitas Multimedia Nusantara (UMN), students can deliver their complaints through an organization named Dewan Keluarga Besar Mahasiswa (DKBM) UMN. In the technological era, complaints management system is also implemented online, called e-complaint. Therefore, a similar online complaint submission system can also be applied at UMN. The method that can be used to support efficient complaints processing is the use of automated classification system because it can save both time and human resources. The evaluation of e-complaint application is conducted using USE Questionnaire. The results of e-complaint application evaluation show that DKBM UMN strongly agrees that e-complaint application is useful, easy to use, easy to learn, and satisfying. In addition, UMN students agree that e-complaint application is useful, easy to use, easy to learn, and satisfying.

PREDIKSI KELAYAKAN MASUK PENJURUSAN IPA SISWA SEKOLAH MENENGAH ATAS MENGGUNAKAN C4.5 (Studi Kasus: SMA Tarakanita Gading Serpong)

Telematika Vol 10, No 2: Agustus (2017)
Publisher : STMIK Amikom Purwokerto

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Abstract

Penjurusan Sekolah Menengah Atas adalah masa yang penting dalam menentukan masa depan pendidikan di jenjang berikutnya, yang dimulai dari kelas 10 naik ke kelas 11, dan untuk menentukan penjurusan tersebut digunakan variabel-variabel penentuan penjurusan. Untuk mendukung hasil prediksi penentuan penjurusan SMA, maka dibuatlah penelitian ini. Pada penelitian ini, algoritma yang digunakan untuk memprediksi kelayakan masuk penjurusan IPA pada Sekolah Menengah Atas, khususnya pada SMA Tarakanita Gading Serpong adalah algoritma C4.5, yang merupakan salah satu algoritma data mining yang populer digunakan. Variabel yang digunakan dalam proses penentuan keputusan adalah nilai materi pelajaran Matematika dan IPA siswa kelas XI, XII, serta hasil psikotest berupa bakat dan minat siswa. Hasil dari algoritma C4.5 ini dapat membentuk suatu pohon keputusan yang dapat menentukan hasil prediksi kelayakan masuk penjurusan IPA. Berdasarkan uji coba yang telah dilakukan, aplikasi prediksi kelayakan masuk penjurusan IPA telah berhasil diterapkan dengan hasil yang sesuai dengan kriteria, dan memiliki tingkat error 16,65%.

Entity Annotation WordPress Plugin using TAGME Technology

TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

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Abstract

The development of internet technology makes more information can be accessed. It makes information need to be organized in order to be easily managed. One solution can be used is by using the entity annotation approach which generates tags to represent that document. In this study, TAGME technology is implemented on a WordPress plugin, which is used to manage a blog. Moreover, information on Wikipedia ‘Bahasa Indonesia’ is processed to generate an anchor dictionary which is required by the technology that is implemented. This plugin performs entity annotation by giving tag suggestion for posts in a blog. Testing is carried out by measuring the precision, recall, and  of tag suggestions given by the plugin. The result shows that the plugin can give tag suggestions with precision 0.7638, recall 0.5508, and  0.59.

Big 5 ASEAN capital markets forecasting using WEMA method

TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

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Abstract

ASEAN through ASEAN Economics Community (AEC) 2020 treaty has proposed financial integration via capital markets integration in order to aim comprehensive ASEAN economic integration. Therefore, the need to have a proper prediction of ASEAN capital market becomes a major issue. In this study, we took big 5 ASEAN capital markets, i.e. Straits Times Index (STI), Kuala Lumpur Stock Exchange (KLSE), Stock Exchange of Thailand (SET), Jakarta Stock Exchange (JKSE), and Philippine Stock Exchange (PSE) to be forecasted using WEMA method. Weighted Exponential Moving Average (WEMA) is a new hybrid moving average method which combines the weighting factor calculation in Weighted Moving Average (WMA) with the procedure of Exponential Moving Average (EMA). WEMA has successfully been implemented and used to forecaste discrete time series data, but never being used to forecast ASEAN capital markets. In this study, we took further action by implementing the WEMA method with brute force approach for scaling factor tuning on big 5 ASEAN capital markets. From the experimental results, we found that WEMA has successfully forecasted all those exchanges. By looking at the forecast error measurement, it gives the best performance on PSE and worst performance on SET dataset among all datasets being considered in this study.