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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : -
Core Subject : Science,
Computer Science & IT
Scientific Journal of Informatics published by the Department of Computer Science, Semarang State University, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : -
Articles 564 Documents
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Analysis Security of SIA Based DSS05 on COBIT 5 Using Capability Maturity Model Integration (CMMI) Handoyo, Eko; Umar, rusydi; Riadi, Imam
Scientific Journal of Informatics Vol 6, No 2 (2019): November 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i2.17387

Abstract

A secure academic information system is part of the college. The security of academic information systems is very important to maintain information optimally and safely. Along with the development of technology, academic information systems are often misused by some irresponsible parties that can cause threats. To prevent these things from happening, it is necessary to know the extent to which the security of the academic information system of universities is conducted by evaluating. So the research was conducted to determine the Maturity Level on the governance of the security of University Ahmad Dahlan academic information system by using the COBIT 5 framework on the DSS05 domain. The DSS05 domain on COBIT 5 is a good framework to be used in implementing and evaluating related to the security of academic information systems. Whereas to find out the achievement of evaluation of academic information system security level, CMMI method is needed. The combination of the COBIT 5 framework on the DSS05 domain using the CMMI method in academic information system security is able to provide a level of achievement in the form of a Maturity Level value. The results of the COBIT 5 framework analysis of the DSS05 domain use the CMMI method to get a Maturity level of 4,458 so that it determines the achievement of the evaluation of academic information systems at the tertiary level is Managed and Measurable. This level, universities are increasingly open to technological developments. Universities have applied the quantification concept in each process, and are always monitored and controlled for performance in the security of academic information systems.
Metode Face Recognition untuk Identifikasi Personil Berdasar Citra Wajah bagi Kebutuhan Presensi Online Universitas Negeri Semarang Kurniawan, Luthfi Maslichul
Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v1i2.4027

Abstract

Salah satu hasil evaluasi dari Sistem Presensi Online Pegawai Universitas Negeri Semarang adalah adanya proses-proses curang di dalam sistem presensi dengan menitipkan presensi atau melakukan foto presensi kosong. Sistem Presensi Online Pegawai yang dikembangkan dengan bahasa pemrograman PHP, basis data MySQL dan pustaka JavaScript JPEGCam itu tidak cukup mampu memberikan gambaran tentang kedisiplinan dari pegawai di lingkungan Universitas Negeri Semarang. Untuk itulah, perlu dibangun sebuah sistem yang mampu mengolah data-data foto yang ditangkap dari proses presensi online tersebut untuk dianalisis apakah ada wajah manusia yang terdeteksi. Maka, dikembangkanlah sebuah sistem face recognition yang dirancangbangun menggunakan bahasa pemrograman Python dan pustaka OpenCV. Hasil dari rancang bangun ini adalah sistem face recognition yang mampu berjalan secara otomatis di komputer server untuk membaca basis data presensi, mengolah foto-foto yang tersimpan pada basis data tersebut, mendeteksi wajah pada foto-foto yang diolah kemudian menampilkan hasilnya pada tabel basis data presensi untuk diolah dalam bentuk skor deteksi wajah yang tampil di rekapitulasi presensi online pegawai. 
Application of Deep Learning Using Convolutional Neural Network (CNN) Method for Women’s Skin Classification Anton, Anton; Nissa, Novia Farhan; Janiati, Angelia; Cahya, Nilam; Astuti, Puji
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.26888

Abstract

Purpose:  Facial skin is the skin that protects the inner part of the face such as the eyes, nose, mouth, and other. The skin of the face consists of some type, among others, normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is hard to get to know and distinguish the type of peel, this is what causes some women’s, it is hard to determine which product cosmetology and proper care for her skin type. Methods: In this study, the method of the Convolutional Neural Network (CNN) is an appropriate method to classify the type of the skin of women of age 20 – 30 years by following a few stages using Python 3.5 with a depth of three layers. In this study, the method used CNN to distinguish the type of skin of the label object of the type of skin that a normal skin type, oily, dry and combination. A combination skin type is composed of normal and dry skin types. Result: The process of learning network CNN to get the results of the value by 67%. As for the classification of Normal skin 100%, the type of the skin of the face 100% Dry, kind of Oily facial skin 100% and combination skin type (Normal and Dry) to 100%. Novelty: It can be concluded that the use of the method of CNN in automatic object recognition in distinguishing the type of leather as a material consideration in determining the object of the image. And the classification method using CNN with the Python program to be able to classify well.
Scheduling Optimization of Sugarcane Harvest Using Simulated Annealing Algorithm Afifah, Eka Nur; Alamsyah, Alamsyah; Sugiharti, Endang
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.14421

Abstract

Scheduling is one of the important part in production planning process. One of the factor that influence the smooth production process is raw material supply. Sugarcane supply as the main raw material in the making of sugar is the most important componen. The algorithm that used in this study was Simulated Annealing (SA) algorithm. SA apability to accept the bad or no better solution within certain time distinguist it from another local search algorithm. Aim of this study was to implement the SA algorithm in scheduling the sugarcane harvest process so that the amount of sugarcane harvest not so differ from mill capacity of the factory. Data used in this study were 60 data from sugarcane farms that ready to cut and mill capacity 1660 tons. Sugarcane harvest process in 19 days producing 33043,76 tons used SA algorithm and 27089,47 tons from factory actual result. Based on few experiments, obtained sugarcane harvest average by SA algorithm was 1651,63 tons per day and factory actual result was 1354,47 tons. Result of harvest scheduling used SA algorithm showed not so differ average from mill capacity of factory. Truck uses scheduling by SA algorithm showed average 119 trucks per day while from factory actual result was 156 trucks. With the same harvest time, SA algorithm result was greater  and the amount of used truck less than actual result of factory. Thus, can be concluded SA algorithm can make the scheduling of sugarcane harvest become more optimall compared to other methods applied by the factory nowdays.
Pengembangan E-Stats Berbasis Web (Studi Kasus Universitas Negeri Semarang) Arini, Florentina Yuni
Scientific Journal of Informatics Vol 1, No 1 (2014): May 2014
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v1i1.3646

Abstract

Saat ini, pengembangan e-learning berbasis web menjadi tren dalam dunia pendidikan. Oleh karena itu, dalam penelitian ini penulis mengembangkan E-Stats berbasis web bidang ilmu statistik yang secara khusus dikembangkan di Universitas Negeri Semarang (UNNES). E-Stats menyediakan layanan akses artikel, jurnal dan materi tentang statistik. Artikel dan abstrak jurnal statistik dapat diakses oleh umum. Sedangkan jika menjadi member memiliki prevelege untuk mendownload jurnal dan materi yang sudah di-upload. Bagi member dapat mendaftar melalui form registrasi yang disediakan di halaman registrasi web. Desain E-Stats melalui tahap prototype (analisis, perancangan, desain sistem, ERD dan desain skema database), pemrograman, pengujian dan pemeliharaan. Melalui E-Stats, pembelajaran statistika dapat diakses online tanpa batas ruang dan waktu. 
A Combination of K-Means and Fuzzy C-Means for Brain Tumor Identification Sari, Christy Atika; Sari, Wellia Shinta; Rahmalan, Hidayah
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.29357

Abstract

Purpose: Magnetic Resonance Imaging is one of the health technologies used to scan the human body in order to get an image of an orgasm in the body. MRI imagery has a lot of noise that blends with the tumor object, so the tumor is quite difficult to detect automatically. In addition, it will be difficult to distinguish tumors from brain texture. Various methods have been carried out in previous studies. Methods: This study combines the K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI. The purpose of the combination is to get the advantages of each algorithm and minimize weaknesses. The method used is Contrast Adjustment using Fast Local Laplacian, K-Means FCM, Canny edge detection, Median Filter, and Morphological Area Selection. The dataset is taken from www.radiopedia.org. Data taken were 73 MRI of the brain, of which 57 MRIs with brain tumors and 16 MRIs of normal brain Evaluation of research results will be calculated using Confusion Matrix. Result: The accuracy obtained is 91.78%. Novelty: K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI.
Poverty Data Model as Decision Tools in Planning Policy Development Mirza, Ahmad Haidar
Scientific Journal of Informatics Vol 5, No 1 (2018): May 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i1.14022

Abstract

Poverty is the main problem in a country both in developing countries to the developed countries, both in structural poverty, cultural and natural. That is, poverty is no longer seen as a measure of the failure of the Government to protect and fulfill the fundamental rights of its citizens but as a challenge of the nation to realize a fair society, prosperous and dignified sovereign. Various efforts have been made in determining government policy measures in an effort to overcome poverty, one of them by conducting a survey to assess the poor. The results of the survey of the various activities of the organization obtained a variety of database versions poverty to areas or locations. The information generated from the poverty database only includes recapitulation of poor people to the area or location. One step is to process the data on poverty in a process of Knowledge Discovery in Databases (KDD) to form a data mining poverty. Data mining is a logical combination of knowledge of data, and statistical analysis developed in the knowledge business or a process that uses statistical techniques, mathematics, artificial intelligence, artificial and machine-learning to extract and identify useful information for the relevant knowledge from various large databases.
An Evaluation Model Using Perceived User Technology Organization Fit Variable for Evaluating the Success of Information Systems Muslimin, Imam; Hadi, Sasongko Pramono; Nugroho, Eko
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.12012

Abstract

In the information systems field, the fit between the components of information systems is a topic that has attracted the attention of many researchers. Various concepts of the fit such as Task-Technology Fit (TTF), Fit between Individuals, Tasks, and Technology (FITT), and Human Organization Technology Fit (HOT-Fit) are proposed and studied in various studies. In those various concept, the fit is one of the keys to the successful implementation and acceptance of information systems. Through a study of relevant literature, this study proposes a model consisting of human, organization, and technology characteristics, and adds the Perceived User Technology Organization Fit (PUTOF) variable as the initiated variable that influences the intention to use. In subsequent research, this model can be tested quantitatively with case studies of the information system implementation in an organization.
A Systematic Review of Machine-vision-based Smart Parking Systems Abidin, Muhammad Zainal; Pulungan, Reza
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25654

Abstract

The development of smart city concept, particularly in smart parking systems, has not solved a problem that occurs in metropolitan areas, such as in urban areas where the population has continued to rise, resulting in high demand for private vehicles and parking spaces. Finding a parking space is known as the most common issue the drivers have had specifically on peak hours’ time. During peak hours, the difficulty arises as many people look around to find vacant parking space at once, which causes many negative impacts on cities and drivers themselves, such as pollution, traffic congestion, traffic accidents, waste of time and fuel, emotions and so on. As a solution, smart parking system exist to equip parking lots with many different types of sensors to automatically detect free parking space that would guide drivers to find the nearest car parking space as efficient as possible. An effective smart parking system can solve this problem and make better use of parking resources. However, many smart parking systems still uses embedded sensors that are expensive for installation and inefficient. This paper presents a review of the existing approaches to the smart parking system. This paper focuses on a machine-vision-based technology used for smart parking system and highlights its main features, advantages and disadvantages.
Prediksi Nilai Tukar Petani Menggunakan Jaringan Syaraf Tiruan Backpropagation Khusniyah, Tri Wardati; Sutikno, Sutikno
Scientific Journal of Informatics Vol 3, No 1 (2016): May 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i1.4970

Abstract

Badan Pusat Statistik (BPS) menggunakan Nilai Tukar Petani (NTP) sebagai salah satu indikator untuk mengukur tingkat kesejahteraan atau kemampuan daya beli petani. Nilai indeks NTP untuk periode yang akan datang perlu di lakukan prediksi yang dapat dimanfaatkan pihak terkait dalam mempersiapkan tindakan-tindakan pencegahan apabila indeks NTP turun dari periode sebelumnya. Paper ini bertujuan untuk mengukur unjuk kerja algoritma jaringan syaraf tiruan Backpropagation dalam memprediksi Nilai Tukar Petani (NTP) Provinsi Jawa Timur satu bulan mendatang. Data yang digunakan yaitu data tahun 2008-2012 untuk proses pelatihan jaringan. Proses pengujian dilakukan dengan membandingkan hasil pengujian dengan data aktual tahun 2013 dan 2014. Hasil pengujian menunjukkan bahwa persentase error terkecil apabila jumlah node lapisan tersembunyi 7 dan nilai laju pembelajaran 0.1 dengan rata-rata error sebesar 0.61% atau tingkat akurasi mencapai 99.39%. 

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