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RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Published by Universitas Abdurrab
ISSN : 24772062     EISSN : 2502891X     DOI : https://doi.org/10.36341/rabit
Core Subject : Science, Engineering,
Computer Science & IT Engineering
This journal is called RABIT, where the name comes from two words namely, RAB which means Abdurrab University and IT which means information technology, it can be interpreted as a journal of this journal Journal of Informatics Engineering Study Program Pekanbaru Abdurrab University. This RABIT journal contains various sciences related to the world of computers especially information technology and information systems, namely, this journal is published twice a year where the initial publication is on January 10 while for the second issue which is on July 10.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 696 Documents
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EVALUASI HOLISTIK KEAMANAN HTTPS PLATFORM E-COMMERCE INDONESIA : INTEGRASI ANALISIS TLS, HTTP SECURITY HEADERS, DAN VULNERABILITY SCANNING Farhani Ayu Amalina; Ruth Hanseliani; Imelda Imelda
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7917

Abstract

The growth of Indonesian e-commerce transactions, which has exceeded 1,200 trillion rupiah, has made online shopping platforms a primary target of cyberattacks, while recurring national data breaches indicate that the presence of HTTPS does not automatically guarantee an adequate level of security. This condition creates a false sense of security, as weak configuration practices—such as vulnerable cipher suites, support for outdated TLS versions, and the absence of HTTP security headers—still open opportunities for attacks. Previous studies have been partial in nature, evaluating only one aspect—TLS/SSL, security headers, or web application vulnerabilities—so no study has assessed all three layers simultaneously in the context of Indonesian e-commerce. Consequently, partial evaluations fail to depict the overall security posture and conceal critical gaps between layers. The novelty of this research lies in its holistic evaluation through the integrated analysis of results from three tools that assess TLS/SSL, HTTP security headers, and vulnerability scanning in a unified manner. This research employs a descriptive-quantitative method with a black-box testing approach through five stages on three Indonesian e-commerce platforms (XX, YY, ZZ) using SSL Labs, Mozilla HTTP Observatory, and OWASP ZAP. The results show that the level of HTTPS security across the three Indonesian e-commerce platforms still varies. Website YY has the best security implementation, with TLS support and security header deployment, although it has application-layer vulnerabilities. Website ZZ has a strong HTTPS implementation but is weak in security headers, while Website XX has the lowest security level as it still supports outdated protocols and has yet to implement several essential security mechanisms. These findings demonstrate that good HTTPS quality does not necessarily guarantee overall system security, making holistic security evaluation highly necessary.
RANCANG BANGUN APLIKASI PENCATATAN HASIL TIMBANGAN TBS BERBASIS DESKTOP DI RUMAH ANGKUT MINYAK (RAM) Erika Binjes Sagala; Ritna Wahyuni; Sri Lestari Rahayu
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7919

Abstract

Oil palm plantations have an important role in supporting the national economy, especially through the production and distribution of Fresh Fruit Bunches (FFB). The weighing process is very important in the operational activities of the Oil Transport House (RAM) because it can determine the value of transactions and production reports. However, many RAM companies still use book-based manual logging systems, which often lead to logging errors, delays in compiling reports, and the risk of data loss. This condition shows that a computerized information system is needed to improve the accuracy and efficiency of recording weighing results. The purpose of this research is to create and develop a desktop-based application that can record the results of weighing FFB in RAM. The system was developed using the Waterfall model of the System Development Lifecycle (SDLC), which consists of planning, analysis, design, and implementation stages with data collection through literature studies, observations, and interviews. The application was developed using the Java NetBeans and MySQL database management systems, and uses data collection methods such as observation, interviews, and literature studies. The results of the study show that the application built is able to produce balance slip bonds, daily, monthly and annual reports, as well as automatically calculate transaction data. System testing using the Black Box Testing method shows that all application features can run according to the features designed and declared valid. Thus, the application developed is able to improve operational efficiency, reduce recording errors, speed up the reporting process, and support more effective processing of FFB balance data at Oil Transport Houses (RAM)
IMPLEMENTASI SEGMENTASI PELANGGAN MENGGUNAKAN ALGORITMA K-MEANS DENGAN MODEL RFM (STUDI KASUS PANDHAWA SEJAHTERA DROPSHIP) Yutia Nia Nesicha; Wiwit Agus Triyanto; Pratomo Setiaji
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7924

Abstract

Pandhawa Sejahtera Dropship is a business in the dropship service sector. This business faces challenges in developing targeted marketing strategies due to the absence of transaction data-based customer segmentation. This study aims to implement customer segmentation using the Recency, Frequency, Monetary (RFM) method and compare the K-Means and Fuzzy C-Means algorithms in grouping customers based on transaction data. The transaction data used amounted to 2,376 records over a one-year period. The methods applied include data preprocessing, RFM value calculation, Min-Max normalization, determination of the optimal number of clusters using the Elbow Method and Silhouette Score, and cluster quality evaluation using the Davies-Bouldin Index (DBI). The results showed that the optimal number of clusters is 3 (k=3) with a Silhouette Score of 0.6242. The three clusters formed are: Cluster 0 (Champions) with 362 customers (25%) characterized by low recency (45.2 days), high frequency (3.8 times), and high monetary (Rp 1,256,780); Cluster 1 (Regular) with 724 customers (50%) characterized by moderate recency (215.3 days), low frequency (1.2 times), and moderate monetary (Rp 345,670); and Cluster 2 (At Risk) with 362 customers (25%) characterized by high recency (345.6 days), very low frequency (1.0 times), and low monetary (Rp 124,890). Based on the method comparison, the K-Means algorithm produced a DBI value of 0.77 and a Silhouette Score of 0.54, better than Fuzzy C-Means with a DBI value of 1.05 and a Silhouette Score of 0.39. Thus, the K-Means algorithm is declared as the best method for customer segmentation on Pandhawa Sejahtera Dropship transaction data. These segmentation results can serve as a basis for developing more targeted and efficient marketing strategies.
KLASIFIKASI SENTIMEN KOMENTAR TWITTER TERHADAP PROGRAM MAKAN BERGIZI GRATIS MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM) KHOIRIYATUL MAGHFIROH; Siti Mujilahwati
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7930

Abstract

Thei Freiei Nutritious Meial Program (MBG), which is deisignateid as onei of thei goveirnmeint’s strateigic policieis, has eiliciteid a widei rangei of public reisponseis, particularly on Twitteir (X). This condition neiceissitateis thei application of a data-drivein approach to eixaminei thei dynamics of public seintimeint as a foundation for policy eivaluation. This study is focuseid on thei deisign and impleimeintation of an LSTM modeil to ideintify seintimeint in public commeints, whilei its peirformancei is eivaluateid using accuracy, preicision, reicall, and F1-scorei meitrics. Thei data analyzeid consisteid of 3,459 commeints obtaineid from Kagglei, which weirei subseiqueintly proceisseid through seiveiral preiproceissing stageis. Thei annotation proceiss was conducteid using a leixicon-baseid approach with thei InSeit Leixicon dictionary, whilei teixt reipreiseintation was constructeid through thei Word2Veic eimbeidding teichniquei. Furtheirmorei, thei dataseit was divideid into training and teisting seits with a proportion of 80:20. Thei labeil distribution showeid a dominancei of positivei seintimeint at 67.0%, followeid by neigativei seintimeint at 23.7% and strongly neigativei seintimeint at 9.3%, reifleicting a teindeincy of public support accompanieid by criticism. Thei LSTM modeil, which was traineid using a configuration of 4 eipochs and a batch sizei of 32, deimonstrateid eixceilleint peirformancei, achieiving an accuracy of 93%, with preicision, reicall, and F1-scorei valueis eiach reiaching 0.93. Theisei reisults indicatei that thei deiveilopeid modeil posseisseis reiliablei classification capability and is suitablei to bei utilizeid as an analytical approach for systeimatically undeirstanding public opinion, theireiby contributing to data-drivein policy eivaluation and deicision-making.
KLASIFIKASI TINGKAT KEMATANGAN BUAH PISANG TANDUK MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK dela_rizqi fitriani; Dela Rizqi Fitriani
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7934

Abstract

This study aims to develop a banana ripeness classification system using a Convolutional Neural Network (CNN) method based on Transfer Learning with MobileNetV2 and EfficientNetB0 architectures. The dataset used consisted of 1,142 banana images divided into training and validation data. The training process applied data augmentation and regularization techniques to improve the model generalization capability. The results showed that the MobileNetV2 model achieved the best performance with an accuracy of 97.75%, precision of 97.84%, recall of 97.75%, and F1-score of 97.74%, while EfficientNetB0 achieved an accuracy of 90.09%. The best model was implemented into a website-based prediction system for automatic and real-time banana ripeness classification. The results indicate that CNN based on Transfer Learning provides excellent performance in identifying banana ripeness levels.
PENERAPAN DATA MINING UNTUK EVALUASI KINERJA PEGAWAI MENGGUNAKAN METODE FUZZY DI PT. ASAM JAWA MEDAN Josua Pranciskus Silalahi; Ismu Alvan Naibaho; Kenny Alfonso Ginting; Muhammad Faris Abqari.F; Allwin M. Simarmata
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7935

Abstract

a manual system, so it often causes subjectivity and is not accurate in describing the performance conditions of employees. With a large number of employees, a system is needed that is able to provide assessments quickly, accurately, and objectively based on several assessment criteria. The research model conducted is a quantitative approach with the Fuzzy Mamdani algorithm-based data mining method using the CRISP-DM framework. The study was conducted on 45 employees based on five input variables, namely discipline, productivity, initiative, rigor, and responsibility. The evaluation process is carried out through the stages of data understanding, data preparation, modeling and evaluation using the Fuzzy Mamdani method. From the results of the study using the Fuzzy Mamdani method, the results of performance evaluation were obtained which were divided into three categories, namely 10 employees with the good category, 34 employees with the adequate category, and 1 employee with the poor category. These results show that the Fuzzy Mamdani method is able to provide a more flexible and objective assessment than the conventional method, so that it can be used as a decision support system in evaluating employee performance. 
PENERAPAN METODE MOORA DALAM PEMERINGKATAN STRATEGI DIGITAL MARKETING TIKTOK SHOP PADA PRODUK FASHION fifin kumalasari; Rini Indriati, M.Kom; Erna Daniati, M.Kom
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7936

Abstract

The growth of social commerce encourages fashion businesses to evaluate their digital marketing strategies in a more measurable manner. Barley Division is a local fashion brand that uses TikTok Shop as a medium for product promotion and sales. However, selecting the most effective strategy requires measurable data rather than relying only on intuition. This study applies the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method to rank TikTok Shop digital marketing strategies for fashion products. The research data were obtained through interviews, observations of marketing activities, and marketing performance documentation, and were then converted into a 0-100 rating scale. The alternatives analyzed include live streaming, product content, affiliate programs, flash sales, and influencers. The evaluation criteria consist of reach, engagement, conversion, and sales, with weights of 30%, 25%, 25%, and 20%, respectively. The analysis stages include constructing the decision matrix, normalizing the data, applying criterion weights, calculating optimization values, and ranking the alternatives. The results show that live streaming obtains the highest preference value of 0.4899, followed by flash sale at 0.4553, affiliate program at 0.4528, product content at 0.4198, and influencer at 0.4124. These results indicate that live streaming is the most effective strategy because it combines reach, direct interaction, and real-time purchase encouragement. This study provides a MOORA-based decision support model that can help business actors prioritize digital marketing strategies objectively and based on measurable data.
KLASIFIKASI KEPUASAN PELANGGAN BERDASARKAN DATA HASIL SURVEI PADA ISP ERATEL MENGGUNAKAN MACHINE LEARNING: CUSTOMER SATISFACTION CLASSIFICATION BASED ON SURVEY DATA AT ISP ERATEL USING MACHINE LEARNING Muhammad Ary Sanjaya Putra Ary; Arif Setiawan; Muhammad Arifin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7937

Abstract

Customer satisfaction is a critical factor in the sustainability of internet service providers (ISP). This study aims to compare the performance of Naïve Bayes, Support Vector Machine (SVM), and Random Forest algorithms in classifying customer satisfaction levels at ISP Eratel based on questionnaire survey data. Data were collected by distributing questionnaires to 301 respondents of Eratel customers with 10 question items on a Likert scale of 1–4 using convenience sampling technique. The research stages include data collection, preprocessing, labeling using the class interval method, stratified dataset splitting with an 80:20 ratio, classification model training, and web dashboard implementation. The labeling process produced three classes: Satisfied (54.82%), Fairly Satisfied (41.20%), and Dissatisfied (3.99%). Based on single split evaluation, the Naïve Bayes and SVM algorithms achieved the same accuracy of 90.16%, with Naïve Bayes showing slightly better performance in recognizing the minority class with a precision of 90.15%, recall of 90.16%, and f1-score of 90.05%, compared to SVM with precision of 87.29%, recall of 90.16%, and f1-score of 88.50%. Meanwhile, Random Forest achieved an accuracy of 86.89% with an f1-score of 86.25%, with a notable advantage in precision for the Dissatisfied class at 100% but a lower recall of 50%. Overall, Naïve Bayes emerged as the best-performing algorithm based on single split evaluation. The classification models were subsequently implemented into a Streamlit-based web dashboard that allows users to upload survey data in Excel (.xlsx) format, display interactive visualizations of customer satisfaction distribution, and spatially map classification results per sub-district in Kudus Regency in real-time. This study concludes that all three algorithms are capable of classifying customer satisfaction effectively, with Naïve Bayes demonstrating the best overall performance based on single split evaluation. Keyword: Customer Satisfaction, Naïve Bayes, Support Vector Machine, Classification, ISP.
ANALISIS VISUAL BRANDING @FORE.COFFEE DALAM MEMBENTUK PERSEPSI DAN KETERLIBATAN AUDIENS GENERASI Z Amanda Aulia Roosadi; Naiza Rosalia
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7938

Abstract

Instagram has become one of the social media platforms used by brands to build brand identity through visual content. Fore Coffee utilizes the Instagram account @fore.coffee to convey its brand identity through consistent and modern visual displays. This study aims to analyze how the visual content of Instagram @fore.coffee shapes Generation Z’s perception of Fore Coffee’s brand identity. This research employed a qualitative approach using a netnography method. Data collection techniques were conducted through interviews, observation, and documentation of Instagram content. Data analysis used the Miles and Huberman interactive analysis model. The findings indicate that the visual content of Instagram @fore.coffee is perceived as modern, organized, and easily recognizable. Promotional content and new menu information tend to attract greater audience attention. However, each audience demonstrated different levels of attention and engagement toward the displayed content. This study indicates that Instagram visual content is related to the formation of audience perceptions toward Fore Coffee’s brand identity.  
PENERAPAN METODE AHP DAN TOPSIS PADA SISTEM PENDUKUNG KEPUTUSAN KINERJA GURU DI SMPN 3 JEKULO Renisa Ramadhani
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7951

Abstract

Teacher performance assessment in schools needs to be conducted objectively, measurably, and in an integrated manner so that the evaluation results can be used as a basis for decision making. SMP Negeri 3 Jekulo Kudus requires a system capable of processing several assessment indicators, such as student questionnaires, student learning outcomes, principal assessments, and teacher attendance. This study aims to build a web-based decision support system to determine teacher performance rankings using the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The AHP method is used to determine the weight of each criterion, while TOPSIS is used to calculate preference scores and determine teacher rankings. Data were obtained through interviews, observations, questionnaires, literature studies, and documentation. The AHP weighting results show that the student questionnaire criterion has the highest weight of 0.4658, followed by student scores of 0.2771, principal assessment of 0.1611, and teacher attendance of 0.0960. The Consistency Ratio value of 0.0115 indicates that the criteria weights are consistent. The TOPSIS results show that the first rank obtained a preference score of 0.8411. The system developed is capable of displaying rankings, preference scores, validating results, and providing recommendations for improving teacher performance. Thus, this system can assist schools in conducting teacher performance evaluations more quickly, transparently, and objectively.

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