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All Journal Foristek Jurnal Teknologi dan Manajemen Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ScientiCO : Computer Science and Informatics Journal BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) The Indonesian Journal of Computer Science
Rizka Ardiansyah
Program Studi S1 Sistem Informasi, Fakultas Teknik, Universitas Tadulako, Indonesia
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Implementasi Algoritma Hybrid Depth-First Search dan Breadth-First Search pada Sistem Repository Dokumen Hasil Karya Mahasiswa (Studi Kasus Pada Jurusan Teknologi Informasi Universitas Tadulako) Ardiansyah, Rizka
ScientiCO : Computer Science and Informatics Journal Vol 1, No 1 (2018): Scientico : April
Publisher : Fakultas Teknik, Universitas Tadulako

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Abstract

Departement of Information Technology Tadulako University, currently does not have a digital repository system that can accommodate the work of student either in form of paper, research report, and scientific posters at the moment. Various of student works is stored in form of physical archives which is definitely risky to get damaged, lost, difficult to access by other students. The existence of a digital repository system can also be used as a benchmark media to develop student creativity in producing scientific work. Searching mechanism is one of the crucial parts in a repository system. There are several commonly used algorithms such as Depth-First search and Breath-First search. Each algorithm have deficiencies and advantages. Therefore, to optimize the searching mechanism on the proposed digital repository system, the author purpose using the DepthFirst search and Breath-First search hybrid algorithms. The result of this study are the design of a prototipe repository system that able to manage and store student scientific documents archive digitally. This system also has a fast and accurate data search fiture.
RANCANG BANGUN SISTEM INFORMASI PENGELOLAAN DATA IKM PADA DINAS PERDAGANGAN DAN PERINDUSTRIAN KOTA PALU BERBASIS WEB Ardiansyah, Rizka; Rahim, Mohammad Arham
ScientiCO : Computer Science and Informatics Journal Vol 2, No 2 (2019): Scientico : November
Publisher : Fakultas Teknik, Universitas Tadulako

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Abstract

Small Micro and Medium Enterprise (IKM) is an industry that operates on a small or medium-scale. Palu city Trade and Industry department has a lot of the IKM data that are manually archived using a large folder and classified according to the year of making the permit or known as the Industrial Business License (SIUI). Aside from being archived manually, the IKM data are also archived electronically using Microsoft Excel. Employees who play a role in managing the IKM data has difficulty in term of searching and filtering process the IKM data for data distribution to the public, besides the number of IKM data is very large and continues to grow every year. IKM data is stored through excel files have a problem against computer viruses, which can make it become corrupt. Related parties from the office expect a system that can manage this IKM data safely, easy to operate (user-friendly), has a better interface (user experience) and fast in carrying out the data distribution process. Our proposed system has managed to run well and as expected in managing data and resolving what happened to the previous conventional method, which is about the distribution and also the safety of SMI data.
ANALISIS SENTIMEN CALON PRESIDEN DAN WAKIL PRESIDEN PERIODE 2019-2024 PASCA DEBAT PILPRES DI TWITTER Ardiansyah, Rizka
ScientiCO : Computer Science and Informatics Journal Vol 2, No 1 (2019): Scientico : April
Publisher : Fakultas Teknik, Universitas Tadulako

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Abstract

Social networking sites, such as Twitter and Facebook are one of the important spaces for political engagement. Twitter or Facebook have become common elements in political campaigns and elections, especially for Indonesia’s presidential election 2019. for the period 2019 - 2024 there are two presidential and vice presidential candidates namely Ir. H. Joko Widodo - Prof. Dr. K.H. Ma'ruf Amin and Lieutenant General (Ret.) H. Prabowo Subianto - H. Sandiaga Uno. B.B.A., M.B.A. the two candidates who ran for the election triggered a lot of related public opinion where the most suitable candidate to become the president of the next period. Public opinion is generally one of the determining factors for presidential candidates who will later win the election. Presidential candidate debate is the efforts of the election commission to facilitate the presidential candidates to introduce their work programs to the public while building public opinion that they are the right people to become leaders of the next period. Although of course, this is not the only major factor that shapes public opinion. The purpose of this study is to summarize the opinions of the people voiced through social media related to the election of candidates for the Indonesian President and Vice President for the period 2019-2024 post debate on the presidential election. While the benefit is to help the community so that they can understand in a broader context such as what the public opinion about presidential candidates, especially on social media Twitter. The results of this study were presidential candidate Joko Widodo - Makruf Amin obtained a 25% positive sentiment, 4.5% negative sentiment and 70.5% neutral sentiment. while the Prabowo Subianto - Sandiaga Uno pair received a 5.1% positive sentiment, 2.5% negative sentiment and 92.4% neutral sentiment.
Pengukuran Performa Algoritma Kendali Kongesti Active Queue Manajemen (AQM) Pada Jaringan Backbone Multicast Berbasis Protocol Multicast PIM-DM Ardiansyah, Rizka
ScientiCO : Computer Science and Informatics Journal Vol 1, No 2 (2018): Scientico : November
Publisher : Fakultas Teknik, Universitas Tadulako

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Abstract

UDP is a data transmission protocol on the Internet that allows data to be sent in realtime and become a development base of various services such as IPTV, VOIP, VOD, and video conferencing. The multicast network was a solution to provide better service to many public needs with the concept of simplicity and efficiency. Dense traffic situation triggering a packet loss and delay that not tolerated by some services, especially multimedia streaming service, while UDP does not guarantee the quality of service so that the necessary traffic management method that serves as a traffic controller to the congestion in the network. This study analyzes the performance of technology based on Active Queue Management (AQM) congestion control in a multicast backbone network based on Protocol Independent Multicast – Dense Multicast using Droptail, Deficit Round Robin (DRR), and Random Early Detection (RED) algorithm, which is simulated using NS2. The simulation results show that the congestion control algorithm, DRR provides the best performance with a balanced value of mean delay, mean throughput, and the mean loss. RED is a congestion control algorithm which has the best queue management mechanism but not able to suppress the mean loss in each scenario tested because RED has a small amount of buffer size, Droptail not an appropriate algorithm for multicast networks implemented in, the absence of an interrupt mechanism trigger data services will have a tendency towards a particular data packet.
NETWORK'S ACCESS LOG CLASSIFICATION FOR DETECTING SQL INJECTION ATTACKS WITH THE LSTM ALGORITHM Hafriadi, Fajar Dzulnufrie; Ardiansyah, Rizka
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2157

Abstract

SQL Injection attacks are one of the popular web attacks. This attack is a network security problem focused on the application layer which is one of the causes of a large number of user data leaks. Currently available SQL detection techniques mostly rely on manually created features. Generally, the detection results of SQL Injection attacks depend on the accuracy of feature extraction, so they cannot overcome increasingly complex SQL Injection attacks on various systems. Responding to these problems, this research proposes a SQL Injection attack detection method using the long short term memory (LSTM) algorithm. The LSTM algorithm can learn data characteristics effectively and has strong advantages in sorting data so that it can handle massive, high-dimensional data. The research results show that the accuracy of the model approach created is able to recognize objects with a high accuracy value of 98% in identifying SQL Injection attacks.
RANCANG BANGUN SISTEM JARINGAN FAILOVER AUTOMATIC TELLER MACHINE (ATM) MELALUI JARINGAN PUBLIK BERBASIS PROTOCOL EOIP DAN P2TP Ardiansyah, Rizka; Pratama, Septiano Anggun; Iskandar, Iskandar; Anshori, Yusuf; Komang, Gusti
Foristek Vol. 14 No. 2 (2024): Foristek
Publisher : Foristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54757/fs.v14i2.584

Abstract

The network on the Automatic Teller Machine currently uses a VSAT (Very Small Aperture Terminal) network based on Geostationary Satellites. VSAT networks often experience problems such as UP-DOWN or total DOWN. Some of the problems that cause interruptions on the VSAT interconnection include the usage period of the Modem electronic device and also ODU (Outdoor Unit) devices such as RFU, LNB, and BUC, ODU devices have a transmit period and also a received period that is uncertain according to the voltage at the location. Problems with the VSAT interconnection system cause ATM services to become unusable, resulting in losses for the bank's profits. In addition, the repair process is also generally quite time-consuming. The purpose of this study is to design an inexpensive ATM backup network solution so that when there is a break/disruption in the VSAT network the service on the ATM is not interrupted and in terms of operational costs does not swell. The backup network is intended to back up the satellite-based leading network automatically so that ATM services are always available when the satellite-based network is interrupted. The result of the study is that the proposed solution can resolve service outages at ATM machines in 34 seconds. The ATM network system will experience disconnection before finally reconnecting through the backup network. The proposed solution is much cheaper than the VSAT-based failover network solution, as it is based on the public internet network.
PENGGABUNGAN METODE SYSTEM USABILITY SCALE DAN USER EXPERIENCE QUESTIONNAIRE UNTUK EVALUASI USABILITY SISTEM INFORMASI MBKM UNIVERSITAS TADULAKO DENGAN PENDEKATAN USER EXPERIENCE Sahril, Sahril; Ardiansyah, Rizka; Wirdayanti, Wirdayanti; Angreni, Dwi Shinta; Yudhaswana, Yuri
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5548

Abstract

Usability merupakan apsek penting dalam mengukur kualitas sebuah sistem. Tingkat Usability sangat dipengaruhi oleh pengalaman pengguna dimana hal ini dapat diukur dengan menilai seberapa cepat dan mudah pengguna mempelajari serta menyelesaikan tugas. Evaluasi usability dikategorikan menjadi pendekatan empiris dan non-empiris. Penelitian ini bertujuan mengevaluasi tingkat usability Sistem Informasi MBKM  dengan pendekatan empiris menggabungkan system usability scale (SUS) dan User Experience Questionnaire (UEQ) sehingga evaluasi yang dilakukan tidak hanya mengukur tingkat usability sebuah sistem namun juga dapat mengidentifikasi  masalah usability berdasarkan aspek pengalaman pengguna. Hasil evaluasi menggunakan SUS menunjukan skor SUS Sistem Informasi MBKM berada pada angka 63 menunjukan skor SUS untuk tingkat adjective scale  pada kategori “OK”, grade scale kategori “C-”, serta acceptability scale berada pada kategori “MARGINAL” dengan net promoter scores kategori “Passive”.  Hasil pengukuran menggunakan UEQ yang telah diadaptasi menunjukan aspek attractiveness (1.20), perspicuity (1.49), efficiency (1.06), stimulation (1.14), dan novelty (0.82) mendapatkan evaluasi positif di atas rata-rata sedangkan aspek dependability (0.88) mendapatkan evaluasi positif namun di bawah rata-rata. Penelitian selanjutnya dapat dilakukan menggunakan pendekatan dan non-empiris dengan melibatkan para ahli (evaluator) untuk menilai tingkat kegunaan serta ngidentifikasi letak masalah dari sebuah sistem
Donor Segmentation Analysis Using the RFM Model and K-Means Clustering to Optimize Fundraising Strategies ., Rezki; Lapatta, Nouval Trezandy; Ardiansyah, Rizka; ., Wirdayanti; Angreni, Dwi Shinta
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8464

Abstract

This study aims to segment donors using the Recency, Frequency, Monetary (RFM) model and the K-Means algorithm to optimize fundraising strategies. The RFM model is used to measure donor engagement through three dimensions: Recency (the last time a donation was made), Frequency (the frequency of donations), and Monetary (the amount of donations). By utilizing RFM scores, donors are then grouped using the K-means algorithm to generate more specific donor segments. This study was conducted using donation data from a non-profit organization, focusing on strategies to improve donor loyalty and donation frequency. The segmentation results identified several key segments, including Loyal Donors, New Donors, Potential Donors, and Low-Priority Donors. Each segment exhibits different donation behavior characteristics and requires a different strategic approach. The implementation of these segmentation results is expected to help the organization design more effective communication strategies and donation programs, as well as improve donor retention and lifetime value. Additionally, this study identifies the potential for enhancing the analytical model for broader applications in the future. This research contributes to non-profit organizations by offering a more efficient approach to managing donor relationships.
APPLICATION OF VGG16 ARCHITECTURE IN WOOD TYPE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK Afiah, Nurul Anggun; Syahrullah, Syahrullah; Ardiansyah, Rizka; Laila, Rahmah; Pohontu, Rinianty
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3874

Abstract

Wood is an important natural resource in construction and the furniture industry, with various types possessing unique characteristics. The selection of wood types is often done manually, which is prone to errors that can negatively impact the working process, product quality, and the sustainability of the forests that source the wood. Therefore, this research aims to improve classification accuracy through the application of technology. This study utilizes Convolutional Neural Network (CNN) with the VGG16 architecture to process images in analyzing the visual characteristics of wood, with the goal of building a model capable of classifying wood types based on images. The dataset used consists of 1,584 samples of wood images sourced from Kaggle. Four models were tested with variations in the training and validation data splits, as well as the use of Adam and Adamax optimizers, over 100 epochs. Model 1 achieved a training accuracy of 96.68% and a testing accuracy of 98.10%. Model 2, with a training accuracy of 99.47% and a testing accuracy of 98.41%, showed the best performance. Models 3 and 4 also yielded testing accuracies of 97.46% and 97.78%, respectively. The results of this study indicate that the application of CNN with the VGG16 architecture can enhance the effectiveness of wood type classification and contribute to more accurate and efficient wood selection practices.
Penerapan Algoritma K-Nearest Neighbor untuk Menentukan Potensi Ekspor Komoditas Pertanian di Provinsi Sulawesi Tengah Ngemba, Hajra Rasmita; Raivandy, I Made Randhy; Hendra, Syaiful; Ardiansyah, Rizka; Dwi Wijaya, Kadek Agus; Nugraha, Deny Wiria; Irfan, Mohamad
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.10235

Abstract

Agriculture is a highly robust sector in Indonesia. This is evidenced in Central Sulawesi Province, where the gross domestic product (GDP) from the agricultural sector, based on constant prices from 2018 to 2021, continues to experience growth. Such conditions suggest that commodities in the agricultural sector have the potential to become export products, enabling a greater economic boost for the region. Before engaging in exports, it is necessary to identify which commodities have potential. One way to determine this is by applying Klassentypology. To simplify the process, it can be implemented in machine learning using the K-Nearest Neighbor algorithm. K-Nearest Neighbor is chosen because this algorithm can handle data containing noise and has good adaptability when given new data. In this research, two machine learning models were developed. The first model is used to classify whether a commodity is advancing or lagging, while the second model is used to classify commodities that grow rapidly and slowly. The highest accuracy obtained from the first model is 96.23%. Meanwhile, the highest accuracy from the second model is 93.49%.
Co-Authors ., Rezki Afiah, Nurul Anggun Ain, Moch. Zukhruf Amriana Andi Hendra Anita Ahmad Kasim Anita, Ayu Deny Wiria Nugraha Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwiwijaya, Kadek Agus Hafriadi, Fajar Dzulnufrie Hajra Rasmita Ngemba Indrajaya, Muhammad Aristo Iskandar Iskandar JULIAN WITJAKSONO Komang, Gusti Lamasitudju, Chairunnisa Lesnusa, Gideon Namlea Maroso, Akwan Mohamad Irfan, Mohamad Mohammad Yazdi Pusadan Muhammad Akbar Mutiara Sari Ngurah, Gusti Nouval Trezandy Lapatta Parenrengi, Andi Fathur Alamsyah A. Pratama, Septiano Anggun Pratiwi, Inten Sakti Putra, Ryan Adi Qofifa, Sitti Nurlaili Rahim, Mohammad Arham Rahmah Laila Raivandy, I Made Randhy Rini Septiani Rinianty, Rinianty Rumampuk, Viola Gracella Ryfial Azhar, Ryfial Sahril Sahril Septiano Anggun Pratama Sondakh, Clivent Gerhard Syahrullah Syahrullah Syaiful Hendra Therry, Alvin Christian Davidson Ulhak, Muhamad Zia Wirdayanti Yazdi Pusadan, Mohammad Yudhaswana, Yuri Yulianti, Indira Yuri Yudhaswana Joefrie Yusuf Anshori