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INDONESIA
Jurnal Informatika
Published by Universitas Ahmad Dahlan
ISSN : 19780524     EISSN : 25286374     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 419 Documents
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Information security analysis on physical security in university x using maturity model Isnaini, Khairunnisak Nur; Solikhatin, Siti Alvi
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a14434

Abstract

The threat of physical security can be from human factors, natural disasters, and information technology itself. Therefore, to prevent threats, we need the right tools to control current activities, evaluate potential impacts, and make appropriate plans so that business processes at X University will not be affected. This research starts by analyzing the problems that arise, followed by collecting the data needed, discussing the results, and making conclusions and recommendations that can be given. The method uses quantitative descriptive research. The research instrument uses interviews and questionnaire techniques. COBIT 5 is used as a framework for measuring the performance that is being implemented and will be achieved. Maturity models are used to measure current and future activities. The goal to be achieved is that the organization can create a physical security environment by the CIA principle (confidentiality, integrity, & availability). Positioning results are at level 3, meaning that the process is currently running in two main standard operating procedures. However, this evaluation specifically on the DSS5.5.5 subdomain (Providing Service Support-Managing physical security for IT Assets) in COBIT 5, and the results are still below the level 3 standard (Established Process), at 2.9 points. So, the right suggestion is to keep activities safe, one of which is to improve facilities and infrastructure, one of which is the use of biometric control in data center management rooms or other rooms with limited access.
The comparison of machine learning methods for the detection of breast cancer Derisma, Derisma; Silvana, Meza
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17077

Abstract

Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python programming and public dataset i.e. MIAS dataset. This dataset has been proven and widely used for a modeling and application of breast cancer classification. Feature extraction used Gray Level Co-occurrence Matrix (GLCM). The machine learning methods that were applied in this study were Decision Tree, SVM, Random Forest, Multilayer Perceptron, KNN, Logistic Regression and Naïve Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 100% accuracy rate and Naïve Beyes was the lowest with 63% of accuracy rate.
The comparison of machine learning methods for the detection of breast cancer Derisma, Derisma; Silvana, Meza
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17077

Abstract

Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python programming and public dataset i.e. MIAS dataset. This dataset has been proven and widely used for a modeling and application of breast cancer classification. Feature extraction used Gray Level Co-occurrence Matrix (GLCM). The machine learning methods that were applied in this study were Decision Tree, SVM, Random Forest, Multilayer Perceptron, KNN, Logistic Regression and Naïve Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 100% accuracy rate and Naïve Beyes was the lowest with 63% of accuracy rate.
Developing support vector regression model to forcast stock prices of mining companies in Indonesia Dhanukhresna Hangga Yudhawan; Tuti Purwaningsih
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17283

Abstract

The modern era as it is now the world of stock investment is in great demand by investors, both long-term and short-term stock investments. Stock investment provides many benefits for investors. To get large profits, investors need to do an analysis in stock investments to predict the price of the shares to be purchased. Very volatile stock price movements make it difficult for investors to predict stock prices. The main hope of investors is to benefit from each price that changes from time to time or can be referred to as time series data. Data mining is a process of extracting large information from a data by collecting, using data, historical patterns of data relationships, and relationships in large data sets. Support vector regression has advantages in making accurate stock price predictions and can overcome the problem of overfitting by itself. PTBA, and ITMG are the leading coal mining companies in Indonesia, so many people want to invest in the company. ADRO, PTBA, and ITMG stock price prediction analysis using support vector regression algorithm has good predictive accuracy values, including. PTBA stock price have an R-square value of 97.9% in the RBF kernel and linear with MAPE respectively of 2,465 and 2,480. And for ITMG stock price it has an R-square accuracy of 94.3% in the RBF kernel and linear with MAPE respectively 5.874 and 5.875. These results indicate that the SVR method is best used for forecasting stock prices.
Developing support vector regression model to forcast stock prices of mining companies in Indonesia Yudhawan, Dhanukhresna Hangga; Purwaningsih, Tuti
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17283

Abstract

The modern era as it is now the world of stock investment is in great demand by investors, both long-term and short-term stock investments. Stock investment provides many benefits for investors. To get large profits, investors need to do an analysis in stock investments to predict the price of the shares to be purchased. Very volatile stock price movements make it difficult for investors to predict stock prices. The main hope of investors is to benefit from each price that changes from time to time or can be referred to as time series data. Data mining is a process of extracting large information from a data by collecting, using data, historical patterns of data relationships, and relationships in large data sets. Support vector regression has advantages in making accurate stock price predictions and can overcome the problem of overfitting by itself. PTBA, and ITMG are the leading coal mining companies in Indonesia, so many people want to invest in the company. ADRO, PTBA, and ITMG stock price prediction analysis using support vector regression algorithm has good predictive accuracy values, including. PTBA stock price have an R-square value of 97.9% in the RBF kernel and linear with MAPE respectively of 2,465 and 2,480. And for ITMG stock price it has an R-square accuracy of 94.3% in the RBF kernel and linear with MAPE respectively 5.874 and 5.875. These results indicate that the SVR method is best used for forecasting stock prices.
SISTEM PENGGAJIAN BERBASIS WEB DI DIRCOMNET YOGYAKARTA Taufiq Ismail; Fuad Thohari
Jurnal Informatika Vol 2, No 1: January 2008
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v2i1.a5231

Abstract

Dircomnet Yogyakarta adalah suatu badan usaha yang bergerak di bidang jasa persewaan akses internet, perawatan komputer, dan pemasangan jaringan internet, memiliki banyak pegawai yang digaji setiap bulannya. Pimpinan sering menangani order pekerjaan secara langsung di luar kantor. Masalah timbul ketika pimpinan membutuhkan laporan penggajian dan memberikan kebijakan penggajian pegawai ketika di luar kantor. Karyawan ingin mengetahui berapa jumlah gaji beserta rinciannya. Berdasar persoalan tersebut, perlu dibangun suatu sistem penggajian real time yang dapat menampilkan laporan penggajian dan memberikan kebijakan penggajian yang dapat diakses dari manapun, serta rinci gaji karyawan yang akan diterima. Penelitian meliputi pengumpulan data dengan studi pustaka, wawancara dan observasi. Kemudian melakukan analisis data, perancangan sistem meliputi DAD dan ERD, perancangan menu, input dan output, serta perancangan WAP. Program dibangun dengan sistem operasi Windows XP, Internet Explorer, MySql front, Dreamweaver MX, Openwave Simulator, Apache, menggunakan bahasa pemrograman PHP, dan terakhir menguji program dengan metode black box test dan alpha test. Hasil penelitian ini diperoleh sistem penggajian berbasis web yang dapat memberikan laporan penggajian kepada pimpinan dimanapun berada, pegawai dapat mengetahui rinci gaji diteriman, dan mengoreksi kesalahan pembayaran gaji. Berdasarkan hasil pengujian program, disimpulkan bahwa program dapat berjalan dengan baik dan sudah memenuhi kebutuhan pemakai serta layak diimplementasikan. Kata kunci : Internet, penggajian,WAP, web.
PEMILIHAN PEMAIN TERBAIK FUTSAL DENGAN METODE SIMPLE MULTI ATTRIBUTE RATING TECNIQUE, STUDI KASUS: TURNAMEN FUTSAL DI SAMARINDA Heliza Rahmania Hatta; Budi Gunawan; Dyna Marisa Khairina
Jurnal Informatika Vol 11, No 1 (2017): Januari
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v11i1.a4679

Abstract

Salah satu peran Sistem Pendukung Keputusan (SPK) dibidang olahraga yaitu untuk pemilihan pemain terbaik futsal dalam turnamen di Samarinda. Sistem ini mengimplementasikan metode SMART (Simple Multi Attribuet Rating Technique). SMART menggunakan linear additive model untuk meramal nilai setiap alternatif. SMART lebih banyak digunakan karena kesederhanaanya dalam merespon kebutuhan pembuat keputusan dan caranya menganalisa respon. Ada beberapa kriteria yang menjadi bahan pertimbangan dalam memilih pemain terbaik antara lain kontribusi kepada tim berupa gol, jumlah pelanggaran, sikap, dan dapat menjadi panutan dalam tim. SPK yang nantinya akan membantu panitia memilihi pemain terbaik dalam jumlah yang banyak dengan hasil perhitungan yang akurat, serta akan memberikan rekomendasi kepada panitia untuk mengetahui pemain yang tepat untuk menjadi pemain terbaik dalam suatu turnamen di Samarinda.  Kata Kunci : Sistem Pendukung Keputusan, Pemain Terbaik, SMART
REVIEW TENTANG VIRTUALISASI Rusydi Umar
Jurnal Informatika Vol 7, No 2: Juli 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v7i2.a2763

Abstract

Virtualisasi adalah cara untuk membuat komputer fisik bertindak bahwa seolah-olah komputer tersebut menjadi dua atau lebih komputer logika, dimana masing-masing komputer logika (nonfisik) mempunyai arsitektur dasar yang sama dengan komputer fisik. Virtualsasi digunakan untuk meningkatkan tingkat utilisasi dari komputer, karena sebagaimana kita ketahui, hampir semua komputer dalam keadaan nganggur (idle). Penggunaan kapasitas cpu berada dibawah 10% bahkan pada komputer server, kecuali pada cpu intensive applications. Paper ini akan membahas tentang mesin virtual (virtual machine), cluster, dan virtual cluster.
PENGEMBANGAN DAN PERANCANGAN TEMPAT TIDUR BAYI (BABY BOX) YANG ERGONOMIS MENGGUNAKAN SOFTWARE AUTOCAD DENGA PENDEKATAN DATA ANTROPOMETRI Agung Kristanto; Sugeng Triyono
Jurnal Informatika Vol 5, No 1: January 2011
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v5i1.a2797

Abstract

Dengan semakin majunya sistem informasi diluar maupun didalamnegeri, Kini masyarakat lebih memperhatikan atau tertarik pada produk-produk yang dihasilkan harus lebih mempunyai nilai ringkas atau flexibledan tentunya sesuai dengan harga dari produk tersebut, Hal ini menjadisebuah acuan bagi pengembang inovasi produk yang mementingkankeinginan masyarakat untuk mendapatkan kekuatan dari kalangan konsumen,Dengan mempertimbangkan usulan dari para orang tua maka penulismencoba menawarkan prototype tempat tidur balita yang sesuai dengandimensi antropometri tubuh bayi di Indonesia. Dengan maksud tempat tidurbayi tersebut mampu memberikan kenyamanan dan keamanan bagi bayi,khususnya kepuasan bagi para orang tua dalam mengasuh atau memfasilitasianaknya. Metodologi penelitian dilakukan dengan penggalian data dariresponden menggunakan metode Quality Funcion Deployment (QFD) untukmengetahui keinginan dari konsumen, serta data antropometri untukmengetahui persentil dari ukuran yang diperlukan untuk merancang TempatTidur Bayi (Baby Box) yang sesuai dengan dimensi tubuh bayi di Indonesia.Hasil penelitian ini dapat diketahui atribut-atribut tempat tidur bayi yangsesuai dengan keinginan pelanggan meliputi : Tempat tidur mampu menahanberat dan gerakan bayi, Dilengkapi dengan kain tile pelindung dari gigitannyamuk, Adanya rak tempat untuk menyimpan pakaian bayi, Warna tempattidur yang cerah, Adanya kantong tas sehingga mudah dibawa. Ukurantempat tidur bayi dikembangkan berdasarkan penerapan data antropometridengan menggunakan persentil 5-th dan 95-th sehingga diperoleh ukurantinggi tempat tidur bayi adalah 80cm, panjang 110 cm, dan lebar 80cm. 
SISTEM PENENTUAN PENERIMA BANTUAN LANGSUNG TUNAI (BLT) DENGAN METODE ANALITYCAL HIRARCHY PROCESS Nur Rochmah Dyah Puji Astuti; Edy Nugroho; Eko Aribowo
Jurnal Informatika Vol 2, No 2: July 2008
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v2i2.a5253

Abstract

Bantuan langsung Tunai adalah program dari pemerintah akibat dicabutnya subsidi BBM dan dialihkan kepada rakyat miskin agar kemiskinan di Indonesia berkurang, dengan adanya BLT ini diharapkan kemakmuran penduduk semakain merata. Penyeleksian masyarakat yang mendapatkan BLT selama ini masih menggunakan cara manual yang dapat memperlambat kerja Badan Pusat Statistik. Untuk mempermudah menyeleksi masyarakat dibutuhkan suatu program aplikasi sistem pendukung keputusan yang dapat membantu dalam mengambil suatu keputusan secara cepat, tepat, dan akurat. Dari penelitian ini dihasilkan program aplikasi sistem pendukung keputusan untuk penerimaan dana BLT dengan metode analitycal hierarchy process. Hasil dari pengujian yang dilakukan terhadap program aplikasi ini telah berjalan dengan baik dan dapat membantu Badan Pusat Statistik dalam proses penerimaan dana BLT. Kata kunci : Metode AHP, Proses Penerimaan Dana BLT

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