cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota malang,
Jawa timur
INDONESIA
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Published by Universitas Brawijaya
ISSN : -     EISSN : -     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering
Jurnal Pengembangan Teknlogi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya merupakan jurnal keilmuan dibidang komputer yang memuat tulisan ilmiah hasil dari penelitian mahasiswa-mahasiswa Fakultas Ilmu Komputer Universitas Brawijaya. Jurnal ini diharapkan dapat mengembangkan penelitian dan memberikan kontribusi yang berarti untuk meningkatkan sumber daya penelitian dalam Teknologi Informasi dan Ilmu Komputer.
Arjuna Subject : -
Articles 6,923 Documents
 164   165   166   167   168 
Klasifikasi Pola Sidik Bibir Untuk Menentukan Jenis Kelamin Manusia Dengan Metode Gray Level Co-Occurrence Matrix Dan Support Vector Machine Eka Novita Shandra; Budi Darma Setiawan; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (515.702 KB)

Abstract

Identification is one way that can be done to recognize individual characteristics. Identification is needed to find out the clarity of personal identity, for both deceased and living people. In the world of forensic medicine, the role of identification is very important. Like fingerprints, lip prints also have unique characteristics for each individual. Lip prints can be used as a means to identify forensic and non-forensic cases. For nonforensic cases, lip prints can determine the sex of an individual. To help in the process of identifying gender based on lip prints, a classification system is needed that can classify the sex of women and men. The process begins with collecting lip print images which are then preprocessed and extracted texture features using the Gray Leveled Co-ocurrence (GLCM) method. There are 4 features that are used namely ASM, Contrast, Correlation and IDM with angles of 0o, 45o, 90o and 135o. Then the feature value is used by data for the training and testing process using the Support Vector Machine (SVM) method. The training data used in the test is 60 data. The results of this study have not provided a good level of accuracy because the system is only able to provide an accuracy of 51.4% by testing the GLCM parameter, namely distance = 1 and SVM parameters λ (lambda) = 0.5, C (complexity) = 1, constant (gamma) = 0.01, and itermax = 100.
Penerapan Algoritme Finite State Machine Berbasis Fragment Shader untuk Proses Pengambilan Keputusan pada Non Player Character (Studi Kasus Game Battle Tank) Muhtadin Ziqi Maulana; Eriq Muh. Adams Jonemaro; Muhammad Aminul Akbar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (907.229 KB)

Abstract

Non player character (NPC) is a character in a game that is not controlled by players, but is controlled through computer programs made by humans. According to the gameAI model the NPC has the ability to make movements and decision making. The battle tank game that was developed by the author in this study also has an NPC developed. there is the game the researcher uses the finite state machine (FSM) algorithm in the decision making process of the NPC. But there is an idea about the application of the FSM algorithm that is by using a shader fragment. With the implementation of the FSM algorithm based on shader fragments, it is expected to get better performance. Because the process of the shader fragment is done in the graphics processing unit (GPU). So that the process carried out can be carried out in parallel between the decision making process and other processes. In applying FSM algorithms based on shader fragment requires three maps, namely world map, agent map and fsm map. After testing the effect of the number of NPCs using 1, 5, 10 and 15 NPCs, respectively, obtained an average yield of 147, 69, 24 and 1 FPS. Whereas for testing the effect of game map size using map sizes of 20x20, 30x30 and 40x40 in succession yielding an average value of 66, 61 and 60 FPS.
Penentuan Waktu Terakhir Penggunaan Ganja dengan Metode Radial Basis Function Neural Network (RBFNN) Sukma Fardhia Anggraini; Sigit Adinugroho; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.692 KB)

Abstract

In 2017, there are 1,742,285 cannabis (popular as marijuana) abusers in Indonesia. If a marijuana addict suddenly wants to stop using marijuana, it can cause symptoms of “sakau”. To anticipate the symptoms of “sakau”, rehabilitation treatment can be taken, so that marijuana addicts can get comprehensive treatment. Determining the appropriate type of rehabilitation, can make it useful. Then knowing the last time abusers had consumption the marijuana, be expected to provide supporting information to determine the appropriate rehabilitation program for marijuana addicts. One technique in data mining that can be used to solve this problem is classification techniques. In this study using Radial Radial Basis Function Neural Network (RBFNN) with K-Means as the classification method. The steps taken included data normalization, K-Means to found the value of centers and spread for Gaussian activation function, training and testing RBFNN. This study using 627 marijuana abuser data which was published on the UCI Machine Learning in 2016. The results of the research showed the optimal parameters involves 7 hidden neurons and 100 as the maximum limit of K-Means iterations. By using these parameters, the classification result achieved accuracy of 35,908%.
Evaluasi Kinerja Pembangunan Program Kerja Base Transceiver Station (BTS) Menggunakan Logical Framework Analysis Studi Pada Badan Aksesibilitas Telekomunikasi dan Informasi (BAKTI) KOMINFO Indah Dwi Chyntia Riswandi; Suprapto Suprapto; Admaja Dwi Herlambang
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (784.652 KB)

Abstract

The population of Indonesia is mostly spread in areas that have not been reached by telecommunications infrastructure. Indonesia's geographical conditions are very diverse, causing infrastructure development to not be carried out optimally. BAKTI is an Indonesian government company that provides ICT services that can be enjoyed by all levels of Indonesian society. BAKTI, especially at the Infrastructure Directorate, created a BTS work program that took place in the Telecommunications Service Blankspot area. The construction of BTS is a real effort by the government to overcome telecommunication gaps felt by the community. This study aims to determine the results of the performance evaluation of the BTS work program using the LFA (Logical Framework Analysis) method, which is useful both in planning, monitoring and management evaluating the development of a work program. LFA consists of seven stages of analysis, that is stakeholder analysis, problem analysis, objective analysis, strategy analysis, log frame (project plan), activity planning, and resources. In the LFA stage, SWOT analysis is conducted to find out how the organization acts in overcoming the problem. The results of this study found that the BTS work program was quite maximal, because the evaluation results showed that there was conformity with the main objectives set by BAKTI. Recommendations are made at each stage of the LFA, so that the recommendations are more specific and can improve performance, and are made based on system information management theories.
Evaluasi dan Perancangan User Interface untuk Meningkatkan User Experience menggunakan Metode Human-Centered Design dan Heuristic Evaluation pada Aplikasi Ezyschool Ikrima Nuha Arifin; Herman Tolle; Retno Indah Rokhmawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (755.95 KB)

Abstract

EzySchool is an application that is intended for parents of students to control the student activities at school. EzySchool has been developed since 2017. Although the EzySchool's User Interface (UI) design is good enough, it needs to be evaluated so that usability aspects are in accordance with User Experience (UX) principle well and correctly. Evaluation is done for determining the success rate of applied UX to meet user needs and satisfaction. The purpose of this study are to find usability problems based on heuristic principles and designing the solution design based on evaluator suggestions, severity ratings and Google Material Design (guidelines). The Human-Centered Design (HCD) approach was used because the design development approach and interactive system focus on the user and user needs. The Heuristic Evaluation (HE) method was used to find and assess usability problems based on heuristic principles with the help of experts as evaluators. The initial data collection was carried out by conducting interviews with EzySchool stakeholder regarding the analysis of the application usage context. Initial heuristic evaluation (involving 3 evaluators) produced 17 problems as a baseline for user needs to design a solution design. Iterate have been done once because of time limitness. The results of this study are a comparison between the initial evaluation and solution design results along with better solution design in UX which is indicated by a decrease of 10 heuristic problems so that only 7 problems were found in the solution design.
Evaluasi Proses Optimasi Risiko, Pengelolaan Keamanan, dan Pengelolaan Layanan Keamanan Menggunakan Kerangka Kerja COBIT 5 Pada PT Tirta Investama (AQUA) Pandaan Vicky Nur Ardianto; Suprapto Suprapto; Admaja Dwi Herlambang
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (637.336 KB)

Abstract

PT Tirta Investama (AQUA) Pandaan is one company that has been utilizing information technology (IT) to support its business process. All activities are managed directly by the division of Danone Information Systems (DAN'IS) as the responsible for the provision and development of technology facilities and corporate information systems. The existence of such utilization, certainly raises the evaluation material in order to maintain the functionality of technology to keep stable. This study aims to evaluate the process of ensure risk optimization, manage security, and manage security services. Two of the three processes are examples of processes related to information security. Information security is selected as an audit object, since the company has a policy on the IS Security Policy document managed by the DAN'IS Security Analyst. This study uses the COBIT 5 framework as the main reference. The research method is doing by observation, interview, and analysis through assessment sheet to describe the condition of Base Practices (BP), Work Product (WP), Generic Practices (GP), and Generic Work Product (GWP) of EDM03 (Ensure Risk Optimization), APO13 (Manage Security), and DSS05 (Manage Security Services). So it is known the capability level of the three processes are at level 3. Each process has different gap levels. Therefore, a recommendation is given as a guide improving the quality of risk optimization and information security so as to reach the targeted level of achievement.
Klasifikasi Pengidap Kanker Payudara Menggunakan Metode Voting Based Extreme Learning Machine (V-ELM) Dheby Tata Artha; Sigit Adinugroho; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (962.841 KB)

Abstract

Breast cancer is a malignant tumor that formed by the abnormal growth of breast cells. Every year, breast cancer causes about 2,1 million women to die. To reduce the number of deaths caused by breast cancer, screening can be chosen for prevention efforts. The development of medical technology and information technology, in the medical world, can be used by researchers in their fields to develop early detection models, from routine consultation data and blood analysis. In this study, breast cancer data will be classified using the Voting Based Extreme Learning Machine (V-ELM). This study using Coimbra Dataset Breast Cancer which published on UCI Machine Learning in 2018. It consists of 116 data, 9 features and 2 classes (Healthy Control and Patient). Firstly, the dataset would be normalized, then began the training process of V-ELM with data train. After that, began the testing process of V-ELM with input values from the training process and data test. The ratio between training data and testing data in this study is 80:20. This study tested several parameters and obtained optimal results, including 20 hidden neurons, the value of k for V-ELM is 35 and the activation function with optimal results is the Sigmoid function. By using those optimal parameters, gives accuracy of 89.56%, sensitivity of 96.924% and specificity of 80%.
Klasifikasi Ujaran Kebencian pada Twitter Menggunakan Metode Naive Bayes Berbasis N-Gram Dengan Seleksi Fitur Information Gain Muhammad Hakiem; Mochammad Ali Fauzi; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (342.938 KB)

Abstract

Hate speech is one of the topics that often discussed in information technology. Hate speech has been usually used by the people that don't like or hate with someone or a group. People stated their hate speech with post it in social media. One of the most used social media to spread the hate speech is Twitter. Hate speech identification is needed to decrease the spread of hate speech. The method used in this research is Naive Bayes based on N-gram and feature selection Information Gain. N-gram features that used in this research are Unigram, Bigram, and combination unigram-bigram. 250 data are used in this research with hate speech label and 250 data with non hate speech label and have 80% proportion for data training and 20% for data testing. The best accuracy results in this research come from Unigram feature and without feature selection Information Gain. The best accuracy result is 84%, precision value 92%, recall value 79,31%, and f-measure value 85,18%. Based on the results obtained it can be concluded that to classify hate speech in Twitter using Naive Bayes has the best result with Unigram feature and without using feature selection Information Gain.
Analisis Sentimen Tentang Kebijakan Ganjil Genap Kendaraan Bermotor di DKI Jakarta Pada Twitter Menggunakan BM25 dan K-Nearest Neighbor Dwi Suci Ariska Yanti; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (927.915 KB)

Abstract

Traffic congestion occurs in many places throughout Indonesia, especially in its capital region of Jakarta. Many strategies have been executed by the government of the capital region as a mean to solve the ongoing traffic congestion problem, one of them is the 'odd-even' policy. On the other note, the problem has inflicted a wide social media complains among Jakarta's residents. In this case, Twitter is considered as a relatively fast and effective social media platform to post opinions used by many Indonesians. Considering its large number of users and easy access to public's opinions, Twitter will have a lot of public's opinions' data which can be used as a material to evaluate the 'odd-even' policy in the capital region of Jakarta. Therefore a method which can separate sentiment from user is needed. It's to answer whether the sentiment is categorized as positive or negative class. In this study, the researcher used BM25 method and K-Nearest Neighbor (KNN) as classifiers. The best test results for f-measure values are 66,1% while the results of accuracy is 66,5%.
Evaluasi Proses Bisnis Menggunakan Metode Quality Evaluation Framework (QEF) (Studi Kasus Bidang Akreditasi Dan Aim Pusat Jaminan Mutu Universitas Brawijaya) Noval Ageng Siswanto; Aditya Rachmadi; Andi Reza Perdanakusuma
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (379.029 KB)

Abstract

Pusat Jaminan Mutu (PJM) is an institution under the auspices of the rector with the objective of ensuring the quality of management system, research, and implementation of education in University of Brawijaya. Quality assurance at PJM is Audit Internal Mutu (AIM) and Accreditation, but in each case there are complaints received by stakeholders. Complaints received are among the activities of the use of auditing systems that are less than the maximum, late report activity, and implementation monitoring program that is not appropriate. From several complaints that have been accepted then the required evaluation of the activities that are divided on the factors of service quality. It is therefore necessary to evaluate the current business process using the Quality Evaluation Framework (QEF). Business process evaluation is done by modeling the current business process first using Business Process Model and Notation (BPMN). Determining the quality factor along with the goal is applied to each indicator of success in business poses, then calculation of target realization is done. The result of calculation has been done and got quality factor that is not in accordance with the target. Quality factors are not suitable will be done root cause analysis by using the technique fishbone diagram and 5 whys.

Page 166 of 693 | Total Record : 6923

 164   165   166   167   168 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol 10 No 13 (2026): Publikasi Khusus Tahun 2026 Vol 10 No 01 (2026): Januari 2026 Vol 10 No 4 (2026): April 2026 Vol 10 No 3 (2026): Maret 2026 Vol 10 No 2 (2026): Februari 2026 Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025 Vol 9 No 12 (2025): Desember 2025 Vol 9 No 11 (2025): November 2025 Vol 9 No 10 (2025): Oktober 2025 Vol 9 No 9 (2025): September 2025 Vol 9 No 8 (2025): Agustus 2025 Vol 9 No 7 (2025): Juli 2025 Vol 9 No 6 (2025): Juni 2025 Vol 9 No 5 (2025): Mei 2025 Vol 9 No 4 (2025): April 2025 Vol 9 No 3 (2025): Maret 2025 Vol 9 No 2 (2025): Februari 2025 Vol 9 No 1 (2025): Januari 2025 Vol 8 No 13 (2024): Publikasi Khusus Tahun 2024 Vol 8 No 10 (2024): Oktober 2024 Vol 8 No 9 (2024): September 2024 Vol 8 No 8 (2024): Agustus 2024 Vol 8 No 7 (2024): Juli 2024 Vol 8 No 6 (2024): Juni 2024 Vol 8 No 5 (2024): Mei 2024 Vol 8 No 4 (2024): April 2024 Vol 8 No 3 (2024): Maret 2024 Vol 8 No 2 (2024): Februari 2024 Vol 8 No 1 (2024): Januari 2024 Vol 7 No 13 (2023): Publikasi Khusus Tahun 2023 Vol 7 No 9 (2023): September 2023 Vol 7 No 8 (2023): Agustus 2023 Vol 7 No 7 (2023): Juli 2023 Vol 7 No 6 (2023): Juni 2023 Vol 7 No 5 (2023): Mei 2023 Vol 7 No 4 (2023): April 2023 Vol 7 No 3 (2023): Maret 2023 Vol 7 No 2 (2023): Februari 2023 Vol 7 No 1 (2023): Januari 2023 Vol 7 No 14 (2023): Antrian Publikasi Vol 6 No 13 (2022): Publikasi Khusus Tahun 2022 Vol 6 No 12 (2022): Desember 2022 Vol 6 No 11 (2022): November 2022 Vol 6 No 10 (2022): Oktober 2022 Vol 6 No 9 (2022): September 2022 Vol 6 No 8 (2022): Agustus 2022 Vol 6 No 7 (2022): Juli 2022 Vol 6 No 6 (2022): Juni 2022 Vol 6 No 5 (2022): Mei 2022 Vol 6 No 4 (2022): April 2022 Vol 6 No 3 (2022): Mei 2022 Vol 6 No 2 (2022): Februari 2022 Vol 6 No 1 (2022): Januari 2022 Vol 5 No 13 (2021): Publikasi Khusus Tahun 2021 Vol 5 No 12 (2021): Desember 2021 Vol 5 No 11 (2021): November 2021 Vol 5 No 10 (2021): Oktober 2021 Vol 5 No 9 (2021): September 2021 Vol 5 No 8 (2021): Agustus 2021 Vol 5 No 7 (2021): Juli 2021 Vol 5 No 6 (2021): Juni 2021 Vol 5 No 5 (2021): Mei 2021 Vol 5 No 4 (2021): April 2021 Vol 5 No 3 (2021): Maret 2021 Vol 5 No 2 (2021): Februari 2021 Vol 5 No 1 (2021): Januari 2021 Vol 5 No 13 (2021) Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020 Vol 4 No 12 (2020): Desember 2020 Vol 4 No 11 (2020): November 2020 Vol 4 No 10 (2020): Oktober 2020 Vol 4 No 9 (2020): September 2020 Vol 4 No 8 (2020): Agustus 2020 Vol 4 No 7 (2020): Juli 2020 Vol 4 No 6 (2020): Juni 2020 Vol 4 No 5 (2020): Mei 2020 Vol 4 No 4 (2020): April 2020 Vol 4 No 3 (2020): Maret 2020 Vol 4 No 2 (2020): Februari 2020 Vol 4 No 1 (2020): Januari 2020 Vol 3 No 12 (2019): Desember 2019 Vol 3 No 11 (2019): November 2019 Vol 3 No 10 (2019): Oktober 2019 Vol 3 No 9 (2019): September 2019 Vol 3 No 8 (2019): Agustus 2019 Vol 3 No 7 (2019): Juli 2019 Vol 3 No 6 (2019): Juni 2019 Vol 3 No 5 (2019): Mei 2019 Vol 3 No 4 (2019): April 2019 Vol 3 No 3 (2019): Maret 2019 Vol 3 No 2 (2019): Februari 2019 Vol 3 No 1 (2019): Januari 2019 Vol 2 No 12 (2018): Desember 2018 Vol 2 No 11 (2018): November 2018 Vol 2 No 10 (2018): Oktober 2018 Vol 2 No 9 (2018): September 2018 Vol 2 No 8 (2018): Agustus 2018 Vol 2 No 7 (2018): Juli 2018 Vol 2 No 6 (2018): Juni 2018 Vol 2 No 5 (2018): Mei 2018 Vol 2 No 4 (2018): April 2018 Vol 2 No 3 (2018): Maret 2018 Vol 2 No 2 (2018): Februari 2018 Vol 2 No 1 (2018): Januari 2018 Vol 2 No 8 (2018) Vol 2 No 6 (2018) Vol 1 No 12 (2017): Desember 2017 Vol 1 No 11 (2017): November 2017 Vol 1 No 10 (2017): Oktober 2017 Vol 1 No 9 (2017): September 2017 Vol 1 No 8 (2017): Agustus 2017 Vol 1 No 7 (2017): Juli 2017 Vol 1 No 6 (2017): Juni 2017 Vol 1 No 5 (2017): Mei 2017 Vol 1 No 4 (2017): April 2017 Vol 1 No 3 (2017): Maret 2017 Vol 1 No 2 (2017): Februari 2017 Vol 1 No 1 (2017): Januari 2017 More Issue