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All Journal IJID (International Journal on Informatics for Development) Jurnal Teknologi Terapan Jurnal Abdi Insani Syntax: Journal of Software Engineering, Computer Science and Information Technology Media Jurnal Informatika Jurnal Pengabdian Masyarakat IPTEK Industrial Research Workshop and National Seminar Journal Of Artificial Intelligence And Software Engineering
Hayati, Hashri
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Other

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Analisis Data Eksplorasi Klasifikasi Aktivitas Otak yang Berbahaya Putriadhinia, Salma Syawalan; Mulia, Syelvie Ira Ratna; Awaludin, Iwan; Sholahuddin, Muhammad Rizqi; Syakrani, Nurjannah; Hayati, Hashri
Prosiding Industrial Research Workshop and National Seminar Vol. 15 No. 1 (2024): Prosiding 15th Industrial Research Workshop and National Seminar (IRWNS)
Publisher : Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/irwns.v15i1.6234

Abstract

Elektroensefalografi (EEG) merupakan alat yang vital dalam rekaman dan analisis aktivitas listrik otak, sering digunakan dalam penelitian dan perawatan medis. Peletakan elektroda EEG mengikuti sistem internasional 10-20, dengan huruf dan angka tertentu untuk menandakan lokasi spesifik di otak. Kualitas pengukuran EEG sangat penting, dengan upaya mengeliminasi artifact yang bisa berasal dari sumber biologis maupun nonbiologis. Monitoring EEG di ICU telah meningkat, terutama untuk mendeteksi pola IIIC yang berbahaya. Pola tersebut sulit dibedakan dari kejang biasa dan dapat menyebabkan kerusakan otak. Penelitian ini bertujuan untuk melakukan analisis terhadap dataset EEG yang memiliki pola IIIC sehingga harapannya dapat berguna untuk peneliti yang hendak menggunakan data tersebut. Penelitian ini menggunakan dataset dari platform Kaggle, tepatnya HMS – Harmful Brain Activity Classification. Dataset tersebut memiliki data mentah EEG dan spektogram yang sudah dianotasi oleh ahli. Analisis data menunjukkan bahwa dataset tersebut memiliki keseimbangan jumlah data yang dianotasi untuk masing-masing kategori IIIC. Dalam dataset tersebut, terdapat data rekaman EEG dan data spektogram yang memiliki nilai kosong (null value) sehingga perlu dilakukan penangan terlebih dahulu sebelum diolah lebih lanjut.
The Relational Data Model on The University Website with Search Engine Optimization Alifi, Muhammad Riza; Hayati, Hashri; Wonoseto, Muhammad Galih
IJID (International Journal on Informatics for Development) Vol. 10 No. 2 (2021): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2021.3223

Abstract

The visibility of a university’s website on the search engine becomes an essential factor to reach a wider audience. One way to improve the visibility of a website is through Search Engine Optimization (SEO). University’s website development with SEO is inseparable from the data model because SEO supporting factors are parts of the consideration in the components and structure of the data model. This study aims to build a data model for a university website accompanied by SEO. The relational data model is used in this study based on the performance and maturity in defining schema-based design. This study was conducted through four sequential stages: literature review, planning, implementation, and evaluation. The resulting relational data model is one that has accommodated four supporting factors for SEO, namely Meta description, Meta keywords, URL structure, and image description. This study has succeeded in building a relational data model at the abstraction level of conceptual and logical.  In the conceptual data model, one entity and 11 attributes are formed. The logical data model was implemented in independent work environments using RelaX and operational requirements can be fulfilled by representing each table or relationship in the schema using relational algebra.
Development of the Shortest Path Navigation Feature in a 360° Virtual Campus Tour Using Dijkstra's Algorithm Alifi, Muhammad Riza; Hodijah, Ade; Setijohatmo, Urip Teguh; Wulan, Sri Ratna; Hayati, Hashri
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6839

Abstract

A 360° virtual campus tour allows users to independently explore all available scenes in the form of 360° panoramic photos through a self-guided navigation feature. However, not all navigation tools provided are capable of generating route recommendations for users to follow. This presents a challenge, as users may feel overwhelmed when deciding where to begin and end the tour—particularly when the number of scenes reaches into the hundreds. In certain scenarios, prolonged interaction within a virtual reality environment may lead to discomfort due to motion sickness. Implementing a shortest path algorithm offers a potential solution by guiding users through recommended routes, thereby improving exploration efficiency and reducing interaction time. This study integrates a shortest path-based navigation feature into a virtual campus tour using Dijkstra’s algorithm, consisting of: (1) a front-end navigation component for the user interface of route searching, and (2) a back-end routing component that processes pathfinding using a graph-based structure. The implemented navigation feature demonstrates high efficiency, with an average execution time of only 4.94 ms and low memory consumption, as measured by a resident set size of 710.47 KB and used heap memory of 668.61 KB.
INFORMATION RETRIEVAL BERBASIS LATENT DIRICHLET ALLOCATION PADA DATA KEKAYAAN INTELEKTUAL Hayati, Hashri; Alifi, Muhammad Riza
Jurnal Teknologi Terapan Vol 11, No 2 (2025): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v11i2.793

Abstract

The shift toward a knowledge-based economy underscores the importance of intellectual property (IP) management. Unfortunately, conventional keyword-based search methods often fail to capture the semantic relationships between concepts in documents—particularly complex ones like patents and copyrights. This study proposes a topic modeling approach using the Latent Dirichlet Allocation (LDA) method to improve the relevance and accuracy of information retrieval in IP data. The research developed 76 models based on four scenarios: with and without language translation, and with and without n-gram tokenization, using topic numbers ranging from 1 to 19. The best four models from each scenario yielded coherence scores between 0.4411 and 0.4581. Evaluation using Mean Average Precision (MAP) on the top 10 documents showed that the model without translation and with unigram tokenization (10 topics) achieved the best results with an average MAP of 78%. The findings indicate that language translation and n-gram tokenization do not significantly impact the coherence score. However, models without n-gram tokenization (bigram and trigram combinations) yielded relatively more semantically relevant search results based on MAP values. Automatic translation in this study resulted in lower MAP scores compared to models without translation.
ANALISIS BRAND LAYANAN AKADEMIK PERGURUAN TINGGI INDONESIA MENGGUNAKAN KLASIFIKASI TEKS DI MEDIA SOSIAL Hayati, Hashri; Alifi, Muhammad Riza
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 1 (2025): Juni 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i1.6924

Abstract

 Penelitian ini bertujuan untuk menganalisis persepsi komunitas eksternal terhadap brand akademik perguruan tinggi di Indonesia melalui media sosial, khususnya Twitter/X. Seiring dengan tingginya jumlah perguruan tinggi dan angka partisipasi kasar (APK), kompetisi antar institusi pendidikan tinggi semakin kuat, mendorong perlunya diferensiasi brand yang disampaikan ke publik. Dalam studi ini, dikumpulkan post dari 30 akun resmi X perguruan tinggi di Indonesia yang kemudian diklasifikasikan ke dalam lima kategori brand akademik: Innovative, Global Impact, Student Engagement, Career Focused, dan Research Excellent. Proses klasifikasi dilakukan dengan membangun model pembelajaran menggunakan algoritma Naïve Bayes, yang diimplementasikan melalui pustaka pemrosesan bahasa alami di lingkungan Node.js. Untuk mengevaluasi kinerja model, dilakukan pengujian terhadap dataset uji terpisah, dan dihitung metrik evaluasi berupa precision, recall, dan accuracy berdasarkan nilai True Positive, False Positive, dan False Negative yang diperoleh melalui confusion matrix untuk setiap kelas. Hasil evaluasi menunjukkan bahwa model yang dikembangkan memiliki performa nilai rata-rata precision sebesar 80,8%, recall sebesar 78,8%, dan accuracy sebesar 80%, sehingga dapat diandalkan sebagai alat bantu untuk memahami kesesuaian antara brand yang dikomunikasikan dan persepsi publik secara daring. Kata Kunci— brand akademik, brand perguruan tinggi, klasifikasi teks, naïve bayes, media sosial. ABSTRACTThis study aims to analyze the perceptions of external communities regarding the academic branding of Indonesian universities through social media, particularly Twitter/X. With the growing number of higher education institutions and rising gross enrollment rates, competition among universities has intensified—prompting the need for more distinct and strategic public brand positioning. In this study, posts were collected from 30 official university X accounts in Indonesia and categorized into five academic brand themes: Innovative, Global Impact, Student Engagement, Career Focused, and Research Excellent. The classification process involved building a supervised machine learning model using the Naïve Bayes algorithm, implemented with a natural language processing library in the Node.js environment. To evaluate the model's performance, a separate test dataset was used, and evaluation metrics—namely precision, recall, and accuracy—were calculated for each class based on values of True Positive, False Positive, and False Negative derived from a confusion matrix. The results indicate that the developed model performs well, achieving average scores of 80,8% for precision, 78,8% for recall, and 80% for accuracy, making it a reliable tool for assessing the alignment between institutional brand communication and public perception in online discourse. Keywords—academic brand, university brand, text classification, naïve bayes, social media.  
PENGEMBANGAN DAN PENDAMPINGAN APLIKASI RAPOR SANTRI BERBASIS WEBSITE DI PONDOK PESANTREN AL-IMAM AL-ISLAMI Alifi, Muhammad Riza; Semiawan, Transmissia; Maspupah, Asri; Hayati, Hashri; Lieharyani, Djoko Cahyo Utomo
Jurnal Abdi Insani Vol 11 No 1 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i1.1203

Abstract

Al-Imam Al-Islami Islamic Boarding School (ponpes) located in Cikembar, Sukabumi, West Java is an educational institution. Al-Imam Al-Islam Islamic Boarding School has been established since 1994. One of the important activities at the Islamic boarding school is managing and issuing (generating) santri passports. Currently, Islamic boarding schools still experience several problems in managing and publishing report cards, including human error because they still use conventional methods using the Excel office application; and the process of issuing report cards is quite long because report card data is not stored in one place. Web application development is aimed at overcoming these problems, through the capability to minimize human error with management support in setting user access rights based on roles or assignments, centralized data storage, and support for the data recapitulation process to speed up the issuance of report cards. This application development method consists of nine stages, namely: (1) Problem Identification; (2) Literature Study; (3) Data Collection; (4) Needs Analysis; (5) Application Design; (6) Application Implementation; (7) Application Testing and Improvement; (8) Assistance in using the application; and (9) Preparation of Output Documentation. The result of this activity is an appropriate technology product in the form of a web application for managing and publishing Islamic boarding school report cards, accompanied by modules and handouts for users using instructions and technical management of the application. Based on test results and use by users, this application has made it easier for Islamic boarding schools to manage and publish Islamic boarding school report cards. As a community service activity, a web-based application for managing and publishing report cards has been adopted and utilized directly by Islamic boarding schools as user partners.
Pelatihan Pembelajaran Computational Thinking Untuk Guru SMP 1 Negeri Baleendah Sari, Aprianti Nanda; Gelar, Trisna; Hayati, Hashri; Firdaus, Lukmannul Hakim; Hodijah, Ade; Alifi, Muhammad Riza
Jurnal Pengabdian Masyarakat IPTEK Vol. 4 No. 1 (2024): Edisi Januari 2024
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/abdi.v4i1.9570

Abstract

Salah satu misi dari SMP Negeri 1 Baleendah adalah melaksanakan proses belajar dan bimbingan secara efektif yang dapat menggali seluruh potensi yang dimiliki siswa sehingga dapat menghasilkan siswa yang berprestasi. Peningkatan prestasi siswa dapat diraih dengan berbagai cara, salah satunya dengan peningkatan kompetensi Computational Thinking (CT). Aktifitas CT dengan format permainan dan multidisiplin dapat meningkatakan kreativitas dari siswa. Pemberian pelatihan aktifitas CT Unlugged seperti Lego-Clone dan Educational Robot dan Plugged dengan pengembangan games, animasi, dan video dengan media Scratch dapat meningkatan kompetensi guru dalam membuat bahan ajar dan media pembelajaran yang kreatif dan menarik. Tahapan pengabdian terdiri dari analisa situasi dan kebutuhan, perancangan bahan ajar pelatihan, pelaksanaan pelatihan, pendampingan peserta pelatihan, evaluasi dan capstone project. Dari hasil evaluasi, kemampuan CT guru yang mengikuti pelatihan meningkat. Selain itu, guru-guru yang mengajar mata Pelajaran berbeda berhasil berkolaboarsi mengembangkan bahan ajar sederhana berbasis CT yang multidisiplin menggunakan Scratch. Selain melakukan pelatihan, Guru berhasil menyelesaikan Capstone Project yang berupa Implementasi CT untuk bahan ajar mulai dari inisiasi ide, pembuatan bahan ajar dan implementasi pada kegiatan belajar mengajar pada masing-masing kelas.
DEVELOPMENT OF A SALES FORECASTING APPLICATION USING THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD WITH EXTERNAL INPUT (ARIMAX) Fauziyyah, Aulia Aziizah; Brahmana, Jonanda Pantas Agitha; Simatupang, Paulina Lestari; Soewono, Eddy Bambang; Hayati, Hashri
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5693

Abstract

that also operates in the culinary industry through the Pempek Duo brand. In the operational business of the culinary sector, PT Selada has developed the Mireta Point of Sale (POS) system as a transactional and reporting tool. However, the existing system has not been equipped with a transaction history data analysis feature to predict sales trends. This condition makes it difficult for the company to identify which products are best-selling and which ones are less popular. This development aims to create a sales forecasting feature based on the Autoregressive Integrated Moving Average with Exogenous Input (ARIMAX) method in the Mireta POS system. The ARIMAX model was chosen because it can incorporate external variables into the prediction calculations, in this case, holiday factors. The development was carried out using a waterfall approach which includes the stages of requirements analysis, system design, model implementation, and accuracy testing. The data used consists of the sales transaction history of Pempek Duo products from January 2022 to February 2023, which has been grouped by week, as well as holiday data as an external variable. The model evaluation results show that the best parameter combination is ARIMAX(1,0,2) with a Mean Absolute Error (MAE) value of 4.3333. This value indicates an average prediction error of 4 sales packages per week. With this feature, Mireta POS can provide more accurate sales predictions, making it easier for the company to identify the best-selling and least popular products.
Application of Named Entity Recognition (NER) in Job Vacancy Matching Using an Ontology-Based Approach (Case Study: Information Technology Sector) Gunawan , Rizki; Hodijah, Ade; Taqwim , Muhammad Ikhsan Maulana; Siti Ababil , Afyar; Setijohatmo, Urip Teguh; Wulan, Sri Ratna; Alifi , Muhammad Riza; Sari , Aprianti Nanda; Hayati, Hashri
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5675

Abstract

The dissemination of job vacancies through online platforms still faces limitations in understanding the semantic relationships between the skills possessed by job seekers and the qualifications required by a job position. This mismatch results in an inefficient search process and longer search times. This study aims to develop a semantic-based job vacancy recommendation application (talent matching) using a skill ontology approach. One of the main challenges in developing the ontology is the lack of standardized data structures in job vacancy postings, particularly in the job description section. To address this issue, Named Entity Recognition (NER) techniques are applied to automatically extract skill entities from job description texts. The extracted results are then classified into a taxonomy structure using SkillsGPT, thereby forming a hierarchical skill concept model semantically represented within the ontology using Protégé. The matching process between user skills and job qualifications is conducted through semantic similarity calculations employing the Sánchez Similarity method. Job vacancy data are collected via web scraping, while system development follows the Rational Unified Process (RUP) methodology and is evaluated using Black Box testing. Evaluation results demonstrate that the developed system is capable of providing semantically relevant job vacancy recommendations according to the user's skill profile. Therefore, this study contributes both theoretically and practically to the development of ontology-based recommendation systems, particularly in the automated modeling of skill taxonomies from unstructured data.
Development of A Sales Forecasting Application Using The Autoregressive Integrated Moving Average Method With External Input (ARIMAX) Fauziyyah, Aulia Aziizah; Brahmana, Jonanda Pantas Agitha; Simatupang, Paulina Lestari; Soewono, Eddy Bambang; Hayati, Hashri
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5693

Abstract

PT Selada Indonesia Produktif is an information technology company that also operates in the culinary industry through the Pempek Duo brand. In the operational business of the culinary sector, PT Selada has developed the Mireta Point of Sale (POS) system as a transactional and reporting tool. However, the existing system has not been equipped with a transaction history data analysis feature to predict sales trends. This condition makes it difficult for the company to identify which products are best-selling and which ones are less popular. This development aims to create a sales forecasting feature based on the Autoregressive Integrated Moving Average with Exogenous Input (ARIMAX) method in the Mireta POS system. The ARIMAX model was chosen because it can incorporate external variables into the prediction calculations, in this case, holiday factors. The development was carried out using a waterfall approach which includes the stages of requirements analysis, system design, model implementation, and accuracy testing. The data used consists of the sales transaction history of Pempek Duo products from January 2022 to February 2023, which has been grouped by week, as well as holiday data as an external variable. The model evaluation results show that the best parameter combination is ARIMAX(1,0,2) with a Mean Absolute Error (MAE) value of 4.3333. This value indicates an average prediction error of 4 sales packages per week. With this feature, Mireta POS can provide more accurate sales predictions, making it easier for the company to identify the best-selling and least popular products.
Co-Authors Ade Hodijah Alifi , Muhammad Riza Asri Maspupah Brahmana, Jonanda Pantas Agitha Djoko Cahyo Utomo Lieharyani Eddy Bambang Soewono Fauziyyah, Aulia Aziizah Firdaus, Lukmannul Hakim Gelar, Trisna Gunawan , Rizki Iwan Awaludin Muhammad Riza Alifi Muhammad Rizqi Sholahuddin Mulia, Syelvie Ira Ratna Putriadhinia, Salma Syawalan Sari , Aprianti Nanda Sari, Aprianti Nanda Setijohatmo, Urip Teguh Simatupang, Paulina Lestari Siti Ababil , Afyar Syakrani, Nurjannah Taqwim , Muhammad Ikhsan Maulana Transmissia Semiawan Wonoseto, Muhammad Galih Wulan, Sri Ratna