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Register: Jurnal Ilmiah Teknologi Sistem Informasi
Published by Universitas Pesantren Tinggi Darul ulum
ISSN : 25030477     EISSN : 25023357     DOI : https://doi.org/10.26594/register
Core Subject : Science,
Computer Science & IT
Register: Scientific Journals of Information System Technology is an international, peer-reviewed journal that publishes the latest research results in Information and Communication Technology (ICT). The journal covers a wide range of topics, including Enterprise Systems, Information Systems Management, Data Acquisition and Information Dissemination, Data Engineering and Business Intelligence, and IT Infrastructure and Security. The journal has been indexed on Scopus (reputated international indexed) and accredited with grade “SINTA 1” by the Director Decree (1438/E5/DT.05.00/2024) as a recognition of its excellent quality in management and publication for international indexed journal.
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Articles 219 Documents
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Peringkasan dokumen berita Bahasa Indonesia menggunakan metode Cross Latent Semantic Analysis Mandar, Gamaria; Gunawan, Gunawan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 3, No 2 (2017): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v3i2.1161

Abstract

Peringkasan dokumen berita Bahasa Indonesia dapat membantu untuk menemukan ide-ide pokok atau informasi penting lain dari sebuah berita. Berita umumnya terdiri atas banyaknya paragraf menjadi sebab diperlukan sebuah sistem untuk mengekstrak informasi, sehingga mampu memberikan ide pokok atau informasi penting yang tepat kepada pembaca, tanpa harus membaca secara detail keseluruhan isi berita tersebut, di samping itu dapat dimanfaatkan guna keperluaan Really Simple Syndication Feed (RSS-Feed). Penelitian ini memaparkan peringkasan dokumen berita berbahasa Indonesia menggunakan metode Cross Latent Semantic Analysis (CLSA) dan Latent Semantic Analysis (LSA). Untuk menguji seberapa baik hasil ringkasan yang dilakukan CLSA penelitian ini menggunakan 240 artikel berita yang diambil dari halaman portal www.kompas.com dan dua pakar yang berlatar belakang bidang yang berbeda. Hasil ringkasan CLSA dengan compression rate 30% memperoleh nilai F-Measure 0.72%. Penelitian ini juga menemukan fakta bahwa CLSA lebih baik dari metode LSA yang merupakan cikal bakal dari metode CLSA, walaupun skor hasil F-Measure keduanya tidak berbeda jauh.  Summarizing news documents in Bahasa serves to find main ideas or any other important information from a piece of news. A system to extract the information from ones consisting of many paragraphs is then deemed necessary in order to present precise main ideas or important information to the readers without them having to read the entire passage of news documents, in addition to become useful for Really Simple Syndication Feed (RSS-Feed). This article discusses summarizing news documents in Bahasa using Cross Latent Semantic Analysis (CLSA). To test if the summary resulted from CLSA qualified, this study examines 240 news articles retrieved from www.kompas.com and employs two experts from different fields. The summary resulted from CLSA with a compression rate of 30% obtains an F-Measure of 0.72%. This study also evidently indicates that CLSA has better performance from Latent Semantic Analysis (LSA) which was the initial system for CLSA, despite both F-Measure percentages being only slightly different.
Land-use suitability evaluation for organic rice cultivation using fuzzy-AHP ELECTRE method Ali, Ircham; Gunawan, Vincensius; Adi, Kusworo
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 7, No 1 (2021): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v7i1.2080

Abstract

Land conversion to organic agriculture is the answer to land degradation problems that interfere with land resources sustainability. An evaluation of land-use suitability is crucial to measure the appropriateness of land for agricultural cultivation. Specifically, organic rice cultivation has some particular standard criteria such as temperature, rainfall, soil depth, pH, c-organic, slope, erosion level, a transition period that influence ranking results, and land suitability classes. Eight organic farmlands were used as alternatives, namely Sawangan, Mangunsari, Tirtosari, Podosoko, Butuh, Krogowanan, Kapuhan, and Jati. Fuzzy Analytic Hierarchy process is used to determine the level of importance of the criteria based on weight assessments by three agricultural experts. The ELECTRE method is applied to rank the most suitable land from several alternatives for organic rice cultivation. The combination of these two multi-criteria decision-making methods complements each other to solve problems in land suitability evaluation. A web-based decision support system (DSS) was created to accelerate data processing integration and present factual information from the land suitability selection process. The implementation of DSS with fuzzy-AHP ELECTRE for evaluating land-use suitability in organic rice cultivation provided the best score for Tirtosari with Ekl=4 and spearman rank correlation the system comparison results with actual data rs=0.95. This study's results indicate that integrating the web with fuzzy-AHP ELECTRE is quite effectively applied for decision-making in organic farming.
Rancang Bangun Sistem Pendukung Keputusan Penentuan Program Acara Di KSTV Kediri Dengan Menggunakan Metode Fuzzy AHP Fatoni, Mufid Ali; Lukmana, Indra; Masrur, Mukhamad
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 1, No 1 (2015): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v1i1.404

Abstract

Penentuan program acara pada suatu stasiun televisi merupakan denyut nadi dari penyiaran pertelevisian. Mekanisme semacam ini harus didukung dengan sistem pendukung keputusan yang bukan hanya mempermudah suatu pekerjaan, tetapi efektifitas dan efisiensinya. Penelitian ini bertujuan untuk menghasilkan sistem pendukung keputusan yang dapat memberikan rekomendasi alternatif program acara sesuai dengan perbandingan kriteria dan alternatif yang telah dievaluasi dengan menggunakan metode Fuzzy AHP. Kriteria yang digunakan pada sistem meliputi biaya produksi, daya tarik, tema, segmentasi, profit, orientasi program, dan etika. Dengan adanya sistem pendukung keputusan ini akan mempermudah divisi program acara dalam menentukan program acara yang akan ditayangkan. Selain itu, sistem juga memberikan kemudahan bagi manager operasional dalam mengawasi acara-acara yang ada dalam proses broadcast KSTV.
Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweet’s Hashtag Priyatno, Arif Mudi; Muttaqi, Muhammad Mirza; Syuhada, Fahmi; Arifin, Agus Zainal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 1 (2019): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v5i1.1382

Abstract

Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot spammer.  Spam spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot spammers.
Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA Dede, Moh.; Asdak, Chay; Setiawan, Iwan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8, No 1 (2022): In progress (January)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2339

Abstract

Land use and land cover (LULC) changes through built-up area expansion always increases linearly with land demand as a consequence of population growth and urbanization. Cirebon City is a center for Ciayumajakuning Region that continues to grow and exceeds its administrative boundaries. This phenomenon has led to peri-urban regions which show urban and rural interactions. This study aims to analyze (1) the dynamics of LULC changes using cellular automata (CA), artificial neural network (ANN), and ANN-CA; (2) the influential factors (drivers); and (3) change probability in the period 2030 and 2045 for Cirebon’s peri-urban. We used logistic regression as quantitative approach to analyze the interaction of drivers and LULC changes. The LULC data derived from Landsat series satellite imagery in 1999-2009 and 2009-2019, validation of dynamic spatial model refers to 100 LULC samples. This research shows that LULC changes are dominated by built-up area expansion which causes plantations and agricultural land to decrease. The drivers have a simultaneous effect on LULC changes with r-square of 0.43, where land slope, distance from existing built-up area, distance from CBD, and accessibility are significant triggers. LULC simulation of CA algorithm is the best model than ANN and ANN-CA based on overall accuracy and overall accuracy (0.96, 0.75, 0.73 and 0.95, 0.66, 0.66 respectively), it reveals urban sprawl through the ribbon and compact development. The average probability of built-up area expansion is 0.18 (2030) and 0.19 (2045). If there is no intervention in spatial planning, this phenomenon will decrease productive agricultural lands in Cirebon's peri-urban.
Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweet’s Hashtag Arif Mudi Priyatno; Muhammad Mirza Muttaqi; Fahmi Syuhada; Agus Zainal Arifin
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol. 5 No. 1 (2019): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v5i1.1382

Abstract

Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot spammer.  Spam spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot spammers.
Block-hash of blockchain framework against man-in-the-middle attacks Riadi, Imam; Umar, Rusydi; Busthomi, Iqbal; Muhammad, Arif Wirawan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2190

Abstract

Payload authentication is vulnerable to Man-in-the-middle (MITM) attack. Blockchain technology offers methods such as peer to peer, block hash, and proof-of-work to secure the payload of authentication process. The implementation uses block hash and proof-of-work methods on blockchain technology and testing is using White-box-testing and security tests distributed to system security practitioners who are competent in MITM attacks. The analyisis results before implementing Blockchain technology show that the authentication payload is still in plain text, so the data confidentiality has not minimize passive voice. After implementing Blockchain technology to the system, white-box testing using the Wireshark gives the result that the authentication payload sent has been well encrypted and safe enough. The percentage of security test results gets 95% which shows that securing the system from MITM attacks is relatively high. Although it has succeeded in securing the system from MITM attacks, it still has a vulnerability from other cyber attacks, so implementation of the Blockchain needs security improvisation.
Software similarity measurements using UML diagrams: A systematic literature review Triandini, Evi; Fauzan, Reza; Siahaan, Daniel O.; Rochimah, Siti; Suardika, I Gede; Karolita, Devi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2248

Abstract

Every piece of software uses a model to derive its operational, auxiliary, and functional procedures. Unified Modeling Language (UML) is a standard displaying language for determining, recording, and building a software product. Several algorithms have been used by researchers to measure similarities between UML artifacts. However, there no literature studies have considered measurements of UML diagram similarities. This paper presents the results of a systematic literature review concerning similarity measurements between the UML diagrams of different software products. The study reviews and identifies similarity measurements of UML artifacts, with class diagram, sequence diagram, statechart diagram, and use case diagram being UML diagrams that are widely used as research objects for measuring similarity. Measuring similarity enables resolution of the problem domains of software reuse, similarity measurement, and clone detection. The instruments used to measure similarity are semantic and structural similarity. The findings indicate opportunities for future research regarding calculating other UML diagrams, compiling calculation information for each diagram, adapting semantic and structural similarity calculation methods, determining the best weight for each item in the diagram, testing novel proposed methods, and building or finding good datasets for use as testing material.
The influence of familiarity and personal innovativeness on the acceptance of fintech lending services: A perspective from Indonesian borrowers Wirani, Yekti; Randi, Randi; Romadhon, Muh Syaiful; Suhendi, Suhendi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2327

Abstract

Financial Technology (FinTech) Lending in Indonesia is an innovative solution for financial services in Indonesia because it has convenience and benefits for Indonesians who need loans, fund management, and other financial transaction activities. FinTech Lending is growing fast because it offers reasonable interest rates and access to conventional financial institutions. The growth of FinTech Lending is expected to support financial inclusion planned by the Indonesian government. In 2020, 126 FinTech Lending Companies were operating illegally by exploiting communities experiencing economic difficulties. This study aims to determine the factors that influence the adoption of FinTech services in Indonesia, considering obstacles and occasions. Factors related to obstacles are Trust and Security in Online Lending platforms, while factors related to occasions are Personal Innovativeness, Interest Rate, and Familiarity. This study used a sampling technique, namely purposive sampling, and involved 85 respondents from Indonesian Borrowers with the age majority between 20 to 25 years old. Processes data obtained from survey results using Partial Least Square-Structural Equation Modeling (PLS-SEM). The results are that Familiarity and Personal Innovativeness affect the acceptance of FinTech Lending companies in Indonesia. In addition, it produces guidance for the improvement of FinTech Lending Companies in Indonesia, which be used to develop and support financial inclusion in Indonesia.
Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA Dede, Moh.; Asdak, Chay; Setiawan, Iwan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2339

Abstract

Land use and land cover (LULC) changes through built-up area expansion always increases linearly with land demand as a consequence of population growth and urbanization. Cirebon City is a center for Ciayumajakuning Region that continues to grow and exceeds its administrative boundaries. This phenomenon has led to peri-urban regions which show urban and rural interactions. This study aims to analyze (1) the dynamics of LULC changes using cellular automata (CA), artificial neural network (ANN), and ANN-CA; (2) the influential factors (drivers); and (3) change probability in the period 2030 and 2045 for Cirebon’s peri-urban. We used logistic regression as quantitative approach to analyze the interaction of drivers and LULC changes. The LULC data derived from Landsat series satellite imagery in 1999-2009 and 2009-2019, validation of dynamic spatial model refers to 100 LULC samples. This research shows that LULC changes are dominated by built-up area expansion which causes plantations and agricultural land to decrease. The drivers have a simultaneous effect on LULC changes with r-square of 0.43, where land slope, distance from existing built-up area, distance from CBD, and accessibility are significant triggers. LULC simulation of CA algorithm is the best model than ANN and ANN-CA based on overall accuracy and overall accuracy (0.96, 0.75, 0.73 and 0.95, 0.66, 0.66 respectively), it reveals urban sprawl through the ribbon and compact development. The average probability of built-up area expansion is 0.18 (2030) and 0.19 (2045). If there is no intervention in spatial planning, this phenomenon will decrease productive agricultural lands in Cirebon's peri-urban.

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