Abstract:Abstract: INET Computer Palembang, as a computer training institution, faces difficulties in understanding participant characteristics due to variations in age, educational background, and chosen course packages. This study…
udy aims to analyze participant criteria and group them based on similarities using the K-Means Clustering algorithm. The data used were historical records of course participants from 2022 to 2025. The research process followed the CRISP-DM stages, starting from data cleaning and transformation, determining the optimal number of clusters using the Elbow Method, to evaluating cluster quality with the Davies-Bouldin Index. The implementation was carried out using Python and the scikit-learn library. The results show that the optimal number of clusters is k=5 with a Sum of Squared Errors (SSE) value of 1064.66 and a Davies-Bouldin Index (DBI) score of 0.820, indicating good cluster quality. The resulting clustering provides a structured profile of participants and demonstrates that K-Means is effective in segmenting course participants. These findings are expected to assist the institution in designing more targeted training programs.
Keywords: clustering; data mining; elbow method; k-means; computer course
Abstract:Abstract: The global transition towards sustainable food systems faces significant challenges in vegetarian food supply chains, including transparency issues, distribution inefficiencies, and quality verification problems.…
s. This research proposes VegeChain development, a decentralized marketplace ecosystem based on smart contracts designed to transform vegetarian food supply chains and accelerate Meatless, Balanced, Green (MBG) program adoption. Using mixed-method methodology integrating blockchain system design, stakeholder analysis, and economic simulation, this research develops a comprehensive technology framework combining blockchain transparency, smart contract automation, and sustainable tokenomics with novel mathematical models. The system implements dynamic pricing algorithms based on Automated Market Maker (AMM) mechanisms, multi-objective optimization for supply chain efficiency, and reputation-based consensus protocols. Simulation results demonstrate that VegeChain implementation can improve supply chain efficiency by 35%, reduce food waste by 28%, and increase consumer trust by 42% measured through validated stakeholder satisfaction surveys (n=456) using 5-point Likert scales with statistical significance p<0.001. Technical innovations include Byzantine Fault Tolerant consensus with 99.9% reliability, gas optimization achieving 67% cost reduction, and real-time quality verification algorithms with 98.7% accuracy.
Keywords: smart contracts; supply chain optimization; automated market makers; blockchain technology; sustainable tokenomics
Abstract:Abstract: Advancements in digital technology have significantly transformed communication and learning. Traditional learning methods have limitations in providing a fast and interactive learning environment, necessitating…
g accessible technology that enhances student and teacher engagement while ensuring convenience. WhatsApp has emerged as a widely used solution due to its accessibility, privacy features, and cross-platform compatibility, offering users a sense of convenience. This study examines the role of Perceived Convenience in the acceptance and use of WhatsApp in secondary education in Medan City using the UTAUT2 Model. A survey was conducted with 439 respondents from 8 secondary schools in Medan and analyzed using SEM-PLS with SmartPLS-4. The results indicate that social influence, hedonic motivation, habit, and perceived convenience positively impact the intention to use WhatsApp. Additionally, facilitating conditions, perceived convenience, and intention to use significantly influence actual usage behavior. However, performance expectancy, effort expectancy, and price value do not affect either intention or behavior in using WhatsApp. Moderating variables such as age, gender, and experience partially moderate the relationships between independent factors and WhatsApp usage intention and behavior. This study contributes by incorporating Perceived Convenience into the UTAUT2 Model and affirming its role in educational technology adoption.
Keywords: perceived convenience; secondary education; UTAUT2; whatsapp
Abstrak: Kemajuan teknologi digital telah membawa perubahan signifikan dalam komunikasi dan pembelajaran. Metode pembelajaran tradisional memiliki keterbatasan dalam menyediakan lingkungan belajar yang cepat dan interaktif, sehingga diperlukan teknologi yang mudah diakses, meningkatkan keterlibatan siswa dan guru, serta nyaman digunakan. WhatsApp menjadi salah satu solusi dan banyak digunakan karena mudah diakses, privasi yang ditawarkan, serta kompatibilitas lintas platform sehingga memberikan kenyamanan yang dapat dirasakan pengguna ketika menggunakannya. Oleh karena itu, Penelitian ini menguji peran Persepsi Kenyamanan terhadap penerimaan dan penggunaan WhatsApp dalam pendidikan menengah di Kota Medan menggunakan Model UTAUT2. Survei dilakukan pada 439 responden dari 8 sekolah menengah di kota Medan, dan dianalisis dengan SEM-PLS menggunakan SmartPLS-4. Hasil penelitian menunjukkan bahwa social influence, hedonic motivation, habit, dan perceived convenience berpengaruh positif signifikan terhadap behavioral intention. Sementara itu, facilitating conditions, perceived convenience, dan behavioral intention berdampak positif pada use behavior. Namun, performance expectancy, effort expectancy, dan price value tidak berpengaruh terhadap niat maupun perilaku penggunaan WhatsApp. Variabel moderasi usia, jenis kelamin, dan pengalaman memoderasi sebagian hubungan antara faktor bebas terhadap niat dan perilaku penggunaan WhatsApp. Penelitian ini berkontribusi dengan menambahkan variabel persepsi kenyamanan (perceived convenience) ke dalam Model UTAUT2 dan menegaskan perannya dalam adopsi teknologi pendidikan.
Kata kunci: pendidikan menengah; persepsi kenyamanan; UTAUT2; whatsapp
Abstract:Abstract: The Halodoc application, as a digital healthcare service platform, has been widely used for various medical purposes, such as doctor consultations, medication purchases, and laboratory services. User interactions…
ns and reviews play a crucial role in enhancing service quality. Sentiment analysis was conducted using the Support Vector Machine (SVM) method to assess user perceptions and satisfaction based on reviews obtained from the Google Play Store platform. The analysis process included data collection, text preprocessing, data transformation using TF-IDF, and training an SVM model to predict sentiment. The model achieved its highest accuracy of 88.32% in the first scenario. However, accuracy slightly decreased in the second and third scenarios, reaching 86.25% and 86.94%, respectively. The analysis results indicated that the model performed best in the first scenario, with the lowest number of prediction errors. Additionally, the model was more accurate in classifying negative and positive sentiments than neutral ones.
Keywords: halodoc application; sentiment analysis; support vector machine algorithm
Abstrak: Aplikasi Halodoc, sebagai platform layanan kesehatan digital, telah banyak digunakan untuk berbagai keperluan medis seperti konsultasi dokter, pembelian obat, dan layanan laboratorium. Interaksi pengguna dan ulasan mereka memiliki peran krusial dalam meningkatkan mutu layanan. Analisis sentimen dilakukan dengan menggunakan metode Support Vector Machine (SVM) untuk mengetahui persepsi dan kepuasan pengguna berdasarkan ulasan yang diperoleh dari Platform Google Play Store. Proses analisis mencakup pengumpulan data, pra-pemrosesan teks, transformasi data menggunakan TF-IDF, dan pelatihan model SVM untuk memprediksi sentimen. Hasil pelatihan model dengan akurasi tertinggi sebesar 88,32% pada skenario pertama. Akurasi sedikit menurun pada skenario kedua dan ketiga, masing-masing sebesar 86,25% dan 86,94%, Hasil analisa menunjukkan bahwa model memiliki performa terbaik pada skenario pertama dengan jumlah kesalahan prediksi terkecil. Selain itu, model cenderung lebih akurat dalam mengklasifikasikan sentimen negatif dan positif dibandingkan netral..
Kata kunci: algoritma support vector machine; analisis sentimen; aplikasi halodoc
Abstract:Abstract: In the digital era of transformation, educational websites serve as vital platforms for transparently disseminating information to stakeholders. This study evaluates the effectiveness of the Jember Regency Education…
ation Department website using the COBIT 5 framework. This study aims to enhance the effectiveness and usability of the Department of Education website in Jember Regency by aligning it with stakeholders' evolving needs through comprehensive evaluation and targeted recommendations. Employing a qualitative descriptive approach, the research identifies challenges such as mobile optimization issues, slow loading times, security vulnerabilities, and content relevance concerns. Despite commendable accessibility, these challenges significantly impact user experience and website credibility. The findings underscore the urgent need for website optimization, improved security measures, and continuous content updates. This research provides actionable recommendations to align the website with IT governance standards, offering a roadmap for enhancement. Furthermore, the MEA Capability Results highlight discrepancies between the current scores and expected standards, indicating the necessity for comprehensive alignment with COBIT 5 guidelines to optimize website functionality and better meet user expectations.Top of Form
Keywords: COBIT 5 framework; Jember Regency Department of Education; website effectiveness
Abstrak: Pada era transformasi digital, situs web pendidikan menjadi platform penting untuk menyebarkan informasi secara transparan kepada para pemangku kepentingan. Studi ini mengevaluasi efektivitas situs web Dinas Pendidikan Kabupaten Jember menggunakan kerangka kerja COBIT 5. Studi ini bertujuan untuk meningkatkan efektivitas dan kegunaan situs web Dinas Pendidikan di Kabupaten Jember dengan menyelaraskannya dengan kebutuhan yang berkembang dari para pemangku kepentingan melalui evaluasi komprehensif dan rekomendasi yang ditargetkan. Dengan pendekatan deskriptif kualitatif, penelitian ini mengidentifikasi tantangan seperti masalah optimasi seluler, waktu muat yang lambat, kerentanan keamanan, dan kekhawatiran tentang relevansi konten. Meskipun aksesibilitasnya baik, tantangan-tantangan ini secara signifikan memengaruhi pengalaman pengguna dan kredibilitas situs web. Temuan ini menegaskan perlunya pengoptimalan situs web, peningkatan langkah-langkah keamanan, dan pembaruan konten yang berkelanjutan. Penelitian ini memberikan rekomendasi yang dapat dilaksanakan untuk menyelaraskan situs web dengan standar tata kelola TI, menawarkan panduan untuk peningkatan. Selain itu, hasil Kemampuan MEA menyoroti perbedaan antara skor saat ini dan standar yang diharapkan, menandakan kebutuhan akan penyesuaian yang komprehensif dengan pedoman COBIT 5 untuk mengoptimalkan fungsionalitas situs web dan memenuhi harapan pengguna dengan lebih baik.
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Kata kunci: Dinas Pendidikan Kabupaten Jember; efektivitas website; kerangka kerja COBIT 5
Abstract:Automatic text summarization is challenging research in natural language processing, aims to obtain important information quickly and precisely. There are two main approach techniques for text summary: abstractive and extractive…
tractive summary. Abstractive Summarization generates new and more natural words, but the difficulty level is higher and more challenging. In previous studies, RNN and its variants are among the most popular Seq2Seq models in text summarization. However, there are still weaknesses in saving memory; gradients are lost in long sentences so resulting in a decrease in lengthy text summaries. This research proposes a Transformer model with an Attention mechanism that can fetch important information, solve parallelization problems, and summarize long texts. The Transformer model we propose is GPT-2. GPT-2 uses decoders to predict the next word using the pre-trained model from w11wo/indo-gpt2-small, implemented on the Indosum Indonesian dataset. Evaluation assessment of the model performance using ROUGE evaluation. The study's results get an average result recall for R-1, R-2, and R-L were 0.61, 0.51, and 0.57, respectively. The summary results can paraphrase sentences, but some still use the original words from the text. Future work increase the amount of data from the dataset to improve the result of more new sentence paraphrases.
Abstract:Abstract: Currently in Indonesia freight forwarding services or expeditions are increasingly being used in business. Companies that provide freight forwarding services are also increasingly popping up, besides that the range…
ange of shipping services is much wider, starting from big cities to small villages, now they are starting to become targets in the business process of freight forwarding services. There are also various kinds of goods that can be sent, ranging from small items, large items such as vehicles, or items that are private in nature such as documents. It is also undeniable that currently goods delivery services have become a mainstay of the community since the pandemic hit. However, in a number of cases with the onset of COVID-19 this can reduce income in goods delivery services due to the emergence of lock down policies in various regions according to the risk map zone for the spread of COVID-19 so that public transportation restrictions must be carried out, therefore it is necessary to carry out and also transformation in technology. One of the companies in the delivery service sector, namely a logistics company, must create a management strategy so that it can survive under the effects of the post-pandemic digital transformation in the logistics sector. In this study, the authors will discuss the business strategies carried out by logistics companies in dealing with the post-COVID-19 pandemic.
Keywords: logistics, strategy management, transformation digital
Abstrak: Saat ini di Indonesia jasa pengiriman barang atau ekspedisi semakin marak digunakan dalam proses bisnis. Perusahaan penyedia jasa layanan pengiriman barang pun semakin banyak bermunculan, selain itu jangkauan pengiriman barang pun jauh lebih luas mulai dari kota-kota besar sampai ke desa-desa kecil sekarang sudah mulai menjadi target dalam proses bisnis jasa layanan pengiriman barang. Barang yang dapat dikirim pun ada berbagai macam mulai dari barang yang kecil, barang yang besar seperti kendaraan, ataupun barang yang bersifat privasi seperti dokumen. Tidak dapat dipungkiri juga bahwa saat ini jasa pengiriman barang menjadi andalan masyarakat semenjak melandanya pandemi. Namun, pada beberapa kasus dengan melandanya COVID-19 ini bisa menurunkan penghasilan pada jasa layanan pengiriman barang dikarenakan munculnya kebijakan lock down di berbagai daerah sesuai dengan zona peta resiko penyebaran COVID-19 sehingga harus dilakukan pembatasan transportasi umum. Perusahaan di bidang jasa pengiriman yaitu perusahaan logistik harus membuat manajemen strategi yang lebih baik agar mampu bertahan di bawah pengaruh pandemic terhadap perusahaan logistic dalam teknologi informasi dalam transformasi digital ERP logistik. Pada penelitian ini penulis akan membahas mengenai strategi-strategi yang dilakukan oleh perusahaan logistic untuk menangani masalah tersebut.
Kata kunci: logistik, manajemen strategi,transformasi digital
Abstract:Abstract: Netflix's success cannot be separated from its investment in building a strong digital industry in its business, including in creating relationships with its customers through social media. Netflix is here and…
becomes a good listener to understand the prevailing pop culture with a variety of interesting content that has been adapted to the data they have regarding the behaviour of their subscribers so that it can be well received by the wider community. This article is explained further in the form of a descriptive analysis of how Netflix, a company that undergoes many transformations, ultimately utilizes social media to build relationships with its customers. It begins with collecting various data relevant to the topic and research title to be presented in a complete presentation through literature studies referred to as sequence data. In the end, it was concluded that Netflix is one of the companies that are indeed successful in using social media as one of their strategies to form a good close relationship with its users, Netflix also maximizes the user data they have such as what customers like and what they don't. which helps Netflix understand each user's behaviour.
Keywords: digital; netflix; social media
Abstrak: Kesuksesan Netflix tidak dapat dilepaskan dari investasi mereka dalam membangun industri digital yang kokoh dalam bisnisnya termasuk dalam menciptakan hubungan dengan para pelanggannya melalui sosial media. Netflix hadir dan menjadi pendengar yang baik untuk memahami kultur pop yang berlaku dengan berbagai konten menarik yang telah disesuaikan dengan data yang mereka miliki terkait perilaku pelanggannya sehingga dapat diterima dengan baik oleh masyarakat luas. Dalam artikel ini, dijelaskan lebih lanjut dalam bentuk analisis deskriptif bagaimana Netflix sebagai sebuah perusahaan yang banyak melakukan transformasi yang pada akhirnya memanfaatkan sosial media untuk menjalin hubungan dengan para pelanggannya. Diawali dengan melakukan pengumpulan berbagai data yang relevan dengan topik dan judul penelitian untuk disajikan menjadi sebuah pemaparan yang utuh melalui kajian-kajian pustaka yang disebut sebagai data sequence. Pada akhirnya, didapatkan kesimpulan bahwa Netflix merupakan salah satu perusahaan yang memang berhasil dalam menggunakan sosial media sebagai salah satu strategi mereka untuk membentuk hubungan kedekatan yang baik dengan para penggunanya, Netflix juga memaksimalkan data pengguna yang mereka miliki seperti apa yang pelanggan sukai dan apa yang tidak pelanggan sukai, yang menunjang Netflix dalam memahami perilaku masing-masing pengguna.
Kata kunci: digital; media sosial; netflix
Abstract:Abstract: The technique of multiclass classification based on SVMs has been widely used. SVM optimization will be accomplished by examining the extraction features of Principal Component Analysis (PCA), Box-Cox Transformation,…
ation, and Recursive Feature Elimination (RFE). The dataset contains 13,611 rows and 17 variables, generated from the UCI repository's multiclass dry bean data. Barbunya, Bombay, Cal, Dermas, Horoz, Seker, and Sira are just a few of the dry bean kinds available. The dataset was tested using SVM Linear kernel and SVM Radial Basis.According to the results, the combination of scale-center-BoxCox-SVM Radial extraction achieves the maximum accuracy of 93.16 percent and the shortest processing time of 6.10 minutes. 96.00 percent, 100 percent, 96.71 percent, 95.16 percent, 97.60 percent, 97.74 percent, and 91.95 percent, according to bean class.RFE-SVM Radial has a 91.18 percent accuracy and a processing time of 6.55 minutes. BoxCox outperforms conventional techniques in terms of prediction accuracy while requiring less training time.
Keywords: Bean, PCA, BoxCox, SVM, RFE
Abstrak: Klasifikasi Multikelas menggunakan SVM telah banyak digunakan. Pada penelitian ini akan diuji fitur ekstraksi Principal Component Analysis, Box Cox Transformation dan fitur eliminisi Recursive Feature Elimination untuk mendapatkan optimasi SVM. Dataset berasal dari data multikelas kacang kering UCI repository dengan jumlah 13.611 baris dan 17 variabel. Kelas kacang kering yakni : Barbunya, Bombay, Cal, Dermas, Horoz, Seker dan Sira. Dataset diuji menggunakan kernel SVM Linier dan SVM Radial Basis. Didapatkan hasil, bahwa kombinasi fitur ekstraksi : scale-center-BoxCox-SVM Radial memiliki akurasi terbaik yakni 93,16% dan waktu proses 6,10 menit. Klasifikasi berdasarkan kelas kacang berturut-turut 96,00%,100%, 96,71%, 95,16%, 97,60%, 97,74% dan 91,95%. RFE- SVM Radial hanya memberikan akurasi sebesar 91,18 % dengan waktu proses sebesar 6.55 menit. Penggunaan BoxCox dibandingkan dengan lainnya, memberikan hasil prediksi lebih baik dan namun tidak mempercepat waktu pelatihan.
Kata kunci: BoxCox; Kacang; PCA; RFE; SVM
Abstract:Abstract: Religious tourism especeeially for Hajj and Umroh is increasingly in demand by the public. Auliya Tour & Travel, which is engaged in the travel agency, has diverse pilgrimage data so that the data collection is…
used to find new knowledge as a marketing strategy using Data Mining techniques. Data Mining is one of the KDD processes that has the function for grouping. K-Means Clustering is a data mining technique that aims to group data into a data subset. The grouping of data on Umroh pilgrims is conducted to find out the interest groups of pilgrims based on age. This study categorizes pilgrim data into three clusters, which are very popular, in high demand and less desirable. Attributes used in processing data include gender, age, and congregation package. Before the data calculation process is carried out, the data transformation process is carried out before data calculation process. Based on data calculations that have been done through the RapidMiner software, the members of the group were very interested, ranging in age from 56 to 83 years, interested groups ranging in age from 29 to 55 years and the group was less interested with an age range 2 to 22 years from 170 records.
Keywords: Clustering, Data Mining, K-Means, RapidMiner, Umroh.
Abstrak: Perjalanan wisata religi khususnya untuk ibadah haji dan umroh semakin diminati masyarakat. Auliya Tour & Travel yang bergerak pada bidang biro perjalanan memiliki data jamaah yang beragam sehingga kumpulan data tersebut dimanfaatkan untuk menemukan pengetahuan yang baru sebagai strategi pemasaran dengan menggunakan teknik Data Mining. Data Mining merupakan salah satu proses dari KDD yang fungsi salah satunya untuk pengelompokan. K-Means Clustering merupakan salah satu teknik Data Mining yang bertujuan untuk mengelompokkan data ke dalam subset data. Pengelompokan data jamaah umroh dilakukan bertujuan untuk mengetahui kelompok minat jamaah berdasarkan usia. Penelitian ini mengelompokkan data jamaah menjadi tiga cluster yaitu sangat diminati, diminati dan kurang diminati. Atribut yang digunakan dalam pengolahan data meliputi jenis kelamin, usia, dan paket jamaah. Sebelum proses perhitungan data dilakukan terlebih dahulu dilakukan proses transformasi data. Berdasarkan perhitungan data yang telah dilakukan melalui software RapidMiner diperoleh anggota kelompok sangat diminati dari rentang usia mulai 56 sampai 83 tahun, kelompok diminati dengan rentang usia mulai 29 sampai 55 tahun dan kelompok kurang diminati mulai usia 2 sampai 22 tahun dari 170 record.
Kata Kunci: Clustering, Data Mining, K-Means, RapidMiner, Umroh