Article Per Year (5 Year)
p-Index From 2020 - 2025
1.665
P-Index
This Author published in this journals
All Journal Semesta Teknika Sinkron : Jurnal dan Penelitian Teknik Informatika J-Intech (Journal of Information and Technology) Jurnal Ilmu Komputer SENTRI: Jurnal Riset Ilmiah Jurnal Komputer Antartika Kohesi: Jurnal Sains dan Teknologi
Fahlapi, Riza
Unknown Affiliation
Aerospace Engineering Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
Prediction of Employee Attendance Factors Using C4.5 Algorithm, Random Tree, Random Forest Fahlapi, Riza; Hermanto, Hermanto; Kuntoro, Antonius Yadi; Effendi, Lasman; Nitra, Ridatu Oca; Nurlela, Siti
Semesta Teknika Vol 23, No 1 (2020): MEI 2020
Publisher : Semesta Teknika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Research on the performance of workers based on the determination of standard working hours for absences conducted by workers in a certain period. In disciplinary supervision, workers are expected to be able to provide the best performance in the implementation of work in accordance with predetermined working hours. The measurement of the level of discipline of admission hours for placement workers is carried out every working day, continuously and continuously. Attendance monitoring already uses online attendance by using data downloaded from the online attendance provider as the main data. In addition, data collection is done by filtering employee absentee data and supporting information on the categories that cause mismatches in meeting work schedules. Mobilization of workers according to location and working hours has been regulated in company regulations allowing the placement of workers in accordance with the residence so as not to affect the desired work results the company is still within reasonable limits and can be increased. The assessment of this study as a progression factor inhibiting the company in achieving company targets. From the results of the author's analysis of the prediction of employee delay factors using three algorithms, namely the C.45 algorithm accuracy = 79.37% and AUC value = 0.646, Random Forest Algorithm accuracy = 78.58% and AUC value = 0.807 while for the Random Tree algorithm accuracy = 76.26% and the AUC value = 0.610.
Twitter Comment Predictions on Dues Changes BPJS Health In 2020 Fahlapi, Riza; Rianto, Yan
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 1 (2020): Article Research, October 2020
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i1.10588

Abstract

The Social Security Administering Body (BPJS) is a facility established by The government in providing services to citizens in The field of health welfare. The Spirit of cooperation in the utilization of health services which is very much currently a constraint in the budget is still insufficient in covering health services as a whole. For this reason, government policy is following with PERPRES No. 75 in 2019, the Government officially raised the BPJS Health contributions for 2020. The increase in BPJS Health contributions certainly caused a lot of comments. Namely Twitter, one of the social media that is used by the public to express disapproval or support for this government policy. This study, testing was carried out related to the prediction of comments from social media on community responses to the increase in BPJS Health contributions taken by the government. In the test carried out 3 (three) input algorithms. For every single algorithm including getting results through the K-NN method with an accuracy of 71.83% and AUC value of 0812, for the Naïve Bayes method produces an accuracy of 81.63% and AUC value of 0586. As for the C 4.5 method, the accuracy is 65.37% and the AUC value is 0628. While testing conducted through the Ensembles Vote method which combines the 3 algorithms above gives the best results with an accuracy of 80.10% and AUC value is 0871 for Twitter comment predictions.
Analisis Kepuasan Pengguna Terhadap Penerapan Aplikasi My Xl Dengan Metode Techhnology Acceptance Model Ashari, Yayan; Supendar, Hendra; Fahlapi, Riza
Jurnal Komputer Antartika Vol. 2 No. 2 (2024): Juni 2024
Publisher : Antartika Media Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70052/jka.v2i2.98

Abstract

Perkembangan pesat teknologi informasi telah membawa dunia masuk ke dalam era baru yang lebih dinamis daripada yang pernah  dibayangkan sebelumnya. Penelitian ini bertujuan untuk Menganalisa tingkat kepuasan pengguna terhadap aplikasi My Xl dan  sejauh mana aplikasi ini memenuhi kebutuhan pengguna. Model yang digunakan untuk menjelaskan kepuasan pengguna terhadap penerapan aplikasi My Xl adalah Technology Acceptance Model (TAM) dengan 3 konstruk yaitu, Perceived Ease of use, Perceived Usefulnes, Behavioral Intention to Use. Data penelitian telah dikumpulkan melalui kuesioner dan dibagi ke 100 responden. Kemudian data diolah dengan analisis regresi linier. Hasil penelitian ini menunjukkan bahwa variabel X1 dan X2 berpengaruh positif dan signifikan terhadap variabel Y. Berdasarkan hasil Uji F pengujian terhadap Variabel Persepsi Kemudahan Pengguna dan Persepsi Kebermanfaatan menunjukkan nilai Sig sebesar 0.000, yang lebih kecil dari tingkat signifikansi 0.05. Oleh karena itu, dapat disimpulkan bahwa kedua variabel tersebut secara bersama-sama memiliki pengaruh positif dan signifikan terhadap Variabel Penerimaan TI.   The rapid development of information technology has brought the world into a new era that is more dynamic than ever imagined before. This study aims to analyze the level of user satisfaction with the MyXl application and the extent to which this application meets user needs. The model used to explain user satisfaction with the application of MyXl is the Technology Acceptance Model (TAM) with 3 constructs namely, Perceived Ease of use, Perceived Usefulness, Behavioral Intention to Use. Research data has been collected through a questionnaire and divided into 100 respondents. Then the data is processed by linear regression analysis. The results of this study indicate that the variables X1 and X2 have a positive and significant effect on the variable Y. Based on the results of the F Test, testing the variables Perception of User Ease and Perception of Usefulness shows a Sig value of 0.000, which is smaller than the significance level of 0.05. Therefore, it can be concluded that these two variables together have a positive and significant influence on the IT Acceptance Variable.
Sistem Informasi Manajemen Stok Berbasis Web Pada Globalindo Group Argomasetyo, Firqi; Alie, Jemmy Yosua; Fahlapi, Riza
Jurnal Komputer Antartika Vol. 2 No. 2 (2024): Juni 2024
Publisher : Antartika Media Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70052/jka.v2i2.303

Abstract

Penelitian ini menyoroti penerapan sistem informasi manajemen stok di Globalindo Group, sebuah perusahaan konveksi besar. Sistem manajemen stok pada Globalindo Group sebelumnya menggunakan catatan kertas serta catatan di ponsel pegawai. Karena masih memakai sistem manual, banyak barang yang hilang di gudang dan di toko, serta susah untuk dilacak atau didata kembali. Lalu dari Tim IT di Globalindo Group dibuatkan manajemen stok berbasis website yang bernama ABSI. Dengan menggunakan sistem manajemen stok berbasis web bernama ABSI ini, Globalindo Group mampu mengelola persediaan barang di gudang dan toko secara lebih efisien. Sistem ini menggantikan metode konvensional yang rentan terhadap kesalahan dan kehilangan data. Penelitian dilakukan menggunakan metode kualitatif, melalui observasi, wawancara, dan studi pustaka. Pengembangan sistem ABSI menggunakan metodologi Software Development Life Cycle (SDLC) dengan model waterfall, yang mencakup perencanaan, analisis, perancangan, pengujian, dan implementasi. Hasilnya menunjukkan bahwa penggunaan ABSI memungkinkan pemantauan persediaan secara real-time, pelacakan pengiriman dan penerimaan barang, serta pengurangan risiko kehilangan barang selama pengiriman. Implementasi sistem ini juga mendukung efisiensi operasional, meningkatkan akurasi data, dan membantu karyawan bekerja lebih efektif. Dengan adanya ABSI, Globalindo Group dapat mencapai target perusahaan lebih mudah dan meningkatkan penghasilan. Penelitian ini merekomendasikan pengembangan lebih lanjut terhadap ABSI, termasuk penambahan fitur dan pembuatan versi mobile untuk kemudahan akses pengguna. Secara keseluruhan, adopsi sistem informasi canggih ini memberikan kontribusi signifikan dalam mendukung pengambilan keputusan yang lebih baik dan meningkatkan kinerja bisnis.   This research highlights the implementation of a stock management information system at Globalindo Group, a large convection company. The stock management system at Globalindo Group previously used paper records and notes on employees' cell phones. Because it still uses a manual system, many items are lost in the warehouse and in the store, and are difficult to track or record again. Then the IT team at Globalindo Group created a website-based stock management called ABSI. By using this web-based stock management system called ABSI, Globalindo Group is able to manage inventory in warehouses and stores more efficiently. This system replaces conventional methods that are prone to errors and data loss. The research was conducted using qualitative methods, through observation, interviews, and literature studies. The development of the ABSI system uses the Software Development Life Cycle (SDLC) methodology with a waterfall model, which includes planning, analysis, design, testing, and implementation. The results show that the use of ABSI enables real-time inventory monitoring, tracking of shipping and receiving goods, and reducing the risk of losing goods during shipping. The implementation of this system also supports operational efficiency, improves data accuracy, and helps employees work more effectively. With ABSI in place, Globalindo Group can achieve company targets more easily and increase revenue. This research recommends further development of ABSI, including the addition of features and the creation of a mobile version for easy user access. Overall, the adoption of this advanced information system makes a significant contribution in supporting better decision-making and improving business performance.
ANALISIS SENTIMEN ULASAN PENGGUNA BINANCE DI GOOGLE PLAY MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE DENGAN TEKNIK SMOTE Fahlapi, Riza; Hafid, Danang Abu; Abdurrazaq, Abdurrazaq; Abulkhoir, Moh. Azam; Kurniawan, Bebi; Garamba, Yafianus
Kohesi: Jurnal Sains dan Teknologi Vol. 6 No. 11 (2025): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v6i11.10616

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Binance di Google Play menggunakan dua algoritma pembelajaran mesin, yaitu Naïve Bayes (NB) dan Support Vector Machine (SVM). Untuk menangani masalah ketidakseimbangan kelas dalam data, diterapkan teknik SMOTE (Synthetic Minority Over-sampling Technique). Data yang digunakan dalam penelitian ini diambil dengan metode web scraping pada ulasan aplikasi Binance, dengan data yang disaring berdasarkan ulasan terbaru dan rating. Hasil eksperimen menunjukkan bahwa dengan penerapan SMOTE, baik algoritma Naïve Bayes maupun SVM memberikan peningkatan yang signifikan pada akurasi serta metrik evaluasi lainnya seperti precision, recall, dan F1-score. Secara keseluruhan, penelitian ini menunjukkan bahwa teknik SMOTE efektif dalam menangani ketidakseimbangan kelas pada analisis sentimen ulasan aplikasi. This study aims to analyze the sentiment of Binance app user reviews on Google Play using two machine learning algorithms, namely Naïve Bayes (NB) and Support Vector Machine (SVM). To overcome the problem of class imbalance in the data, the SMOTE (Synthetic Minority Over-sampling Technique) technique is applied. The data used in this study was taken using the web scraping method on Binance app reviews, with data filtered based on the latest reviews and ratings. The experimental results show that with the application of SMOTE, both the Naïve Bayes and SVM algorithms provide significant improvements in accuracy and other evaluation metrics such as precision, recall, and F1-score. Overall, this study shows that the SMOTE analysis technique is effective in handling class synchrony in app review sentiment.
ANALISIS SENTIMEN KOMENTAR YOUTUBE TERKAIT PENERAPAN MAKAN BERGIZI GRATIS MENGGUNAKAN MODEL ALGORITMA SVM Riwanto, Muhammad Hilmy; Ardhiyansyah, Pramudhitya; Adiansyah, Riski Abimur; Alfiansyah, Afif; Waek, Gregorius; Fahlapi, Riza
Kohesi: Jurnal Sains dan Teknologi Vol. 6 No. 12 (2025): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v6i12.10912

Abstract

Penerapan program makan bergizi gratis adalah langkah yang strategis meningkatkan kesejahteraan masyarakat, terutama kelompok rentan. Penelitian ini menganalisis persepsi sentimen masyarakat terhadap program tersebut melalui komentar YouTube menggunakan algoritma “Support Vector Machine (SVM).”Data diperoleh melalui web scraping komentar dari video YouTube yang relevan. Tahapan analisis meliputi pengumpulan data, pra-pemrosesan (pembersihan teks, penghapusan kata tidak relevan, tokenisasi), dan ekstraksi fitur menggunakan TF-IDF. SVM digunakan untuk mengklasifikasikan komentar menjadi sentimen positif, negatif, dan netral, dengan evaluasi menggunakan presisi, recall, dan F1-score. Hasil menunjukkan akurasi model sebesar 84,17%. Sebagian besar komentar bernada positif, mencerminkan dukungan publik, meski ada kritik terkait distribusi dan sosialisasi program. Komentar netral cenderung informatif tanpa opini eksplisit. Penelitian ini memberikan wawasan penting bagi evaluasi program oleh pemerintah, membantu meningkatkan kualitas implementasi melalui respon publik yang lebih terukur. Analisis sentimen berbasis algoritma terbukti efektif dalam memahami data teks berskala besar dan tidak terstruktur. The implementation of the free nutritious meal program is a strategic step to improve community welfare, particularly for vulnerable groups. This study analyzes public sentiment perceptions of the program through YouTube comments using the "Support Vector Machine (SVM)" algorithm. Data was collected through web scraping of relevant YouTube video comments. The analysis phases include data collection, preprocessing (text cleaning, removal of irrelevant words, tokenization), and feature extraction using TF-IDF. SVM was employed to classify comments into positive, negative, and neutral sentiments, with evaluation metrics including precision, recall, and F1-score. The results showed a model accuracy of 84.17%. Most comments were positive, reflecting public support, although there were criticisms related to program distribution and socialization. Neutral comments were generally informative without explicit opinions. This study provides valuable insights for government program evaluations, helping to improve implementation quality through more measurable public responses. Algorithm-based sentiment analysis has proven effective in understanding large-scale and unstructured text data.
Perbandingan Algoritma Klasifikasi Analisis Sentimen Pengguna Aplikasi Getcontact Dalam Pencegahan Penipuan Online Hermanto, Hermanto; Fahlapi, Riza; Kuntoro, Antonius Yadi; Asra, Taufik
J-INTECH (Journal of Information and Technology) Vol 12 No 1 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i1.1262

Abstract

Online fraud refers to various fraudulent acts carried out over the internet with the aim of fraudulently obtaining financial gain or personal information. We need to continue to spread awareness about the importance of security for ourselves and the people we know, where currently there are many different modes of online fraud. One application that is well known to the public is the GetContact application, which is an application designed to provide information about incoming calls, identify spam or fraudulent calls, and provide services related to a list of telephone contacts that have been registered by fellow users of the application. In this research, researchers will analyze the sentiment of comments from users of the Getcontact application by comparing the test results of classification algorithms, namely Naïve Bayes Classifier and SVM. This research process will begin with data sampling using the scrapping technique on Google Playstore and processing data from users of the Getcontact application using RapidMiner. After the preprocessing process and model testing with two textmining methods using algorithms, namely SVM and Naive Bayes, the evaluation and validation results show that Naïve Bayes has a higher level of accuracy than SVM. For Naïve Bayes, the accuracy value reached 82.97% with an AUC value of 0.500, while for SVM, the accuracy value was 78.00% with an AUC value of 0.926. These results show that Naïve Bayes is superior in classifying user comments on the Getcontact application on Google Play as positive and negative comments.
Analisis Sentimen Analisis Sentimen Terhadap Twitter Direktorat Jenderal Bea dan Cukai Menggunakan komparasi Algoritma Naïve Bayes dan Support Vector Machine Saputra, Dedi Dwi; Fahlapi, Riza; Kuntoro, Antonius Yadi; Hermanto, Hermanto; Asra, Taufik
J-INTECH (Journal of Information and Technology) Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i02.1274

Abstract

Direktorat Jenderal Bea & Cukai (DJBC) is a government agency in charge of guarding and serving export and import activities in Indonesia since its establishment in 1946 which is expected by the community as the front guard in protecting the community in this field, but in recent times there have been many cases involving the institution of the Directorate General of Customs & Excise which make this institution can affect the view of the performance of this institution. With the description of the problem above, it is very interesting to conduct research on public views using tweets from twitter @bravobeacukai and @beacukaiRI which are owned and processed by Direktorat Jenderal Bea & Cukai as a place to channel public opinions and views on this institution. This research uses the Smote method with Naïve Bayes and compared with Support vector machine methods for these results to compare the level of accuracy. The results of this study found that using the Smote method with Naïve Bayes obtained an Accuracy value of 78.95%, Precision 74.01%, Recall 89.41%, and AUC 0.650 while for the Smote method with Support vector machine is worth 73.35% Accuracy, Precision 67.88%, Recall 88.95%, and AUC 0.853. Based on the research results, the smote method with Naïve Bayes has the greatest results and is effective with the dataset studied.
PERAN SENSOR OLFACTORY DALAM MEMUNCULKAN EMOSI PERASAAN DAN KENANGAN: EKSPLORASI FRAGRANCE DYNAMICS DALAM MEREKAM KEMBALI MEMORI WAKTU Faqih, Muhammad Bayu; Fahlapi, Riza
SENTRI: Jurnal Riset Ilmiah Vol. 3 No. 6 (2024): SENTRI : Jurnal Riset Ilmiah, Juni 2024
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v3i6.3003

Abstract

Manusia sebagai makhluk hidup yang paling berkembang, memiliki sistem penciuman yang sangat aktif dengan puluhan bahkan ratusan reseptor penciuman yang beragam. Kompleksitas ini jauh lebih tinggi dibandingkan dengan indra lainnya dalam hal jumlah rangsangan fisik yang berbeda dan juga makhluk hidup lainnya. Kemampuan manusia untuk membedakan berbagai bau, secara konsisten mengidentifikasinya, dan memunculkan kenangan tentang peristiwa romantis tertentu, sangat terkait erat. Aroma dapat mempengaruhi cara kita menarik perhatian orang di sekitar kita. Dalam beberapa situasi, pria dapat tertarik pada wanita yang menggunakan produk dengan aroma yang meningkatkan hasrat seksual. Di sisi lain, wanita cenderung tertarik pada pria yang memiliki aroma alami yang serupa dengan aroma mereka sendiri
Perancangan UI/UX Design Warung Pintar Berbasis Android Menggunakan Metode Design Thinking (Studi Kasus: Warung 16) Kuntoro, Antonius Yadi; Fahlapi, Riza; Saputra, Dedi Dwi; Hermanto, Hermanto; Sukmawati, Alfiani; Asra, Taufik
Jurnal Ilmu Komputer (JUIK) Vol 5, No 2 (2025): JUNE 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i2.4168

Abstract

Perkembangan teknologi informasi yang pesat telah menjangkau berbagai kalangan, termasuk instansi swasta, negeri, wirausahawan, pebisnis, sekolah, hingga perguruan tinggi. Teknologi informasi tidak hanya mempermudah pencarian informasi, tetapi juga mendukung kelancaran bisnis, termasuk aktivitas penjualan. Peran User Interface (UI) dan User Experience (UX) menjadi krusial dalam pengembangan aplikasi untuk memastikan kenyamanan dan kemudahan pengguna. Aplikasi mobile memungkinkan pengguna mengakses informasi, media sosial, dan marketplace online dengan mudah. Warung 16, toko retail di Bogor, melayani pembelian langsung dan delivery via WhatsApp. Namun, metode WhatsApp memiliki kendala seperti ketiadaan informasi stok real-time dan proses pemesanan manual. Penelitian ini merancang UI/UX aplikasi untuk mempermudah pembeli dalam proses pembelian dan akses informasi produk di Warung 16. Desain ini bertujuan meningkatkan efisiensi, mengurangi kesalahan manual, dan menawarkan pengalaman pengguna yang optimal. Melalui pendekatan Design Thinking pada aplikasi warung 16 yang diuji dengan motede System Usability Scale (SUS) terhadap 112 responden menggunakan kuesioner mendapatkan hasil sebesar 91,2 yang menunjukkan bahwa desain memenuhi persyaratan dengan baik dalam uji kegunaan.
Co-Authors Abdurrazaq Abdurrazaq, Abdurrazaq Abulkhoir, Moh. Azam Adiansyah, Riski Abimur Alfiansyah, Afif Alie, Jemmy Yosua Antonius Yadi Kuntoro Ardhiyansyah, Pramudhitya Argomasetyo, Firqi Ashari, Yayan Faqih, Muhammad Bayu Garamba, Yafianus Hafid, Danang Abu Hermanto Hermanto Kurniawan, Bebi Lasman Effendi Nitra, Ridatu Oca Rianto, Yan Riwanto, Muhammad Hilmy Saputra, Dedi Dwi siti nurlela Sukmawati, Alfiani Taufik Asra Waek, Gregorius