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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Journal La Multiapp Indonesian Journal of Innovation Studies
Moningkey, Efraim
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Law, Crime, Criminology & Criminal Justice

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Geospatial Validation for Task Letter Automation in Tomohon City: Validasi Geospasial untuk Otomatisasi Surat Tugas di Kota Tomohon Moningkey, Efraim; Atuna, Annisa Salsabilah; Santa, Kristofel
Indonesian Journal of Innovation Studies Vol. 26 No. 4 (2025): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v26i4.1745

Abstract

General background. Digital transformation is central to modernizing public services and improving administrative reliability. Specific background. At the Tomohon City Land Office, manual task letter issuance and attendance monitoring often cause delays and errors. Knowledge gap. Previous research largely focused on GPS-based attendance systems without integrating automated task letter generation. Aims. This study aims to develop a web-based information system integrating task letter automation and geospatial attendance validation using the Haversine algorithm. Results. The system automatically generates task letters, embeds geolocation data, and verifies officer attendance within a specified radius in real time. Testing confirmed accurate distance calculations, reduced administrative errors, and improved task monitoring. Novelty. The integration of Haversine-based geospatial validation with administrative automation in the land sector represents a unique contribution to digital governance. Implications. The system provides a scalable model for modernizing bureaucratic processes and supports Indonesia’s e-government initiatives through accurate, real-time monitoring of field activities. Highlight Development of a web-based system integrating task letter automation and geospatial validation Accurate attendance verification through the Haversine algorithm in real time Supports bureaucratic modernization and e-government initiatives in the land sector KeywordWeb Based Information System, Haversine Algorithm, Task Assignment, Attendance Monitoring, E-Government
Sistem Klasifikasi Tingkat Kematangan Cabai Rawit Menggunakan Algoritma K-Nearest Neighbor (KNN) Ananta, Asti; Kumajas, Sondy C.; Moningkey, Efraim
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 9 No. 3 (2025): IKRAITH-INFORMATIKA Vol 9 No 3 November 2025
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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Abstract

Cabai rawit melrulpakan salah satul komoditas pelrtanian belrnilai elkonomi tinggi di Indonelsia, namuln pelnelntulan tingkat kelmatangannya masih dilakulkan selcara manulal olelh peltani selhingga selring melnyelbabkan keltidakkonsistelnan dalam prosels paneln. Pelnellitian ini melngelmbangkan sistelm klasifikasi tingkat kelmatangan cabai rawit melnggulnakan algoritma K-Nelarelst Nelighbor (KNN) belrbasis fitulr warna HSV (Hulel, Satulration, Valulel). Data citra cabai dipelrolelh langsulng dari pelrkelbulnan dan diprosels mellaluli tahap prelprocelssing, elkstraksi fitulr HSV, pellatihan modell, hingga implelmelntasi dalam aplikasi belrbasis welb melnggulnakan Flask. Sistelm mampul melngklasifikasikan cabai kel dalam tiga katelgori, yaitul melntah, seltelngah matang, dan matang. Modell KNN delngan nilai k=3 melnghasilkan akulrasi selbelsar 86% belrdasarkan pelnguljian melnggulnakan data ulji. Hasil pelnellitian melnulnjulkkan bahwa algoritma KNN dapat digulnakan selcara elfelktif dalam klasifikasi tingkat kelmatangan cabai rawit selrta dapat melndulkulng prosels paneln dan distribulsi selcara lelbih objelktif dan konsisteln.
Implementation K-Means Algorithm in Promotional Media Destination Tour Minahasa Web Based Moningkey, Efraim; Rantung, Vivi Peggie; Surbakti, Peliks Andreas
Journal La Multiapp Vol. 7 No. 1 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i1.2659

Abstract

Minahasa Regency has great tourism potential with a variety of destinations including cultural, natural, and man-made tourism. However, tourism promotion efforts still face obstacles due to the lack of integrated media capable of grouping destination information based on tourist interests and preferences. This study aims to apply the K-Means clustering algorithm in web-based promotional media to group Minahasa tourist destinations based on the level of user interaction, which is represented by the number of likes and comments on promotional content for each destination. The research method is carried out through several stages, namely collecting tourist destination data, pre-processing interaction data, implementing the K-Means algorithm with a specified number of clusters of three according to the main categories of tourismcultural, natural, and man-made), and implementing the clustering results into a web-based evaluation system that uses the Silhouette Coefficient to evaluate the quality of cluster formation. The results show that the K-Means algorithm is able to effectively group tourist destinations into three clusters that reflect the level of popularity, making it easier for users to find destination recommendations according to their interests. Implementation in a web-based system also provides an interactive display in the form of a list of destinations per cluster and recommendations for popular destinations. Thus, this study proves that the application of K-Means can increase the effectiveness of Minahasa tourism promotion, and in the future it can be developed with the integration of real-time data from social media and comparison with other clustering algorithms.
Sentiment Analysis of the Song Lost - Bring Me the Horizon Based on Reviews on YouTube Using the SVM Algorithm Moningkey, Efraim; Sombo, Wanki Dwi Warsun
Journal La Multiapp Vol. 7 No. 2 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i2.2685

Abstract

Bring Me The Horizon's song "Lost" received a wide response on YouTube, evident in the thousands of comments containing a variety of responses ranging from support and criticism to neutral opinions. The rapid development of social media has made it easier for people to freely share their views and experiences on musical works without being bound by space and time. YouTube, as one of the largest video-sharing platforms, plays a crucial role in documenting public perception of the song. This study was conducted to analyze listener sentiment towards the song "Lost" based on YouTube comments using the Support Vector Machine (SVM) algorithm and the Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction technique. Comments were collected through web scraping from the song's official video, then processed through case folding, punctuation removal, tokenizing, stopword removal, and stemming to produce clean and uniform data. Term weights were calculated using TF-IDF and then used to label positive, negative, and neutral sentiments. The SVM model was built from training data and tested with test data to evaluate its performance using accuracy, precision, recall, and f1-score metrics so that classification quality could be assessed comprehensively. Based on the test results, the SVM algorithm was able to classify listener comments with 94% accuracy, with a distribution of negative sentiment of 207 comments, neutral comments of 1,280, and positive comments of 732. These findings demonstrate the effectiveness of SVM in analyzing the sentiment of song comments on social media and provide a more comprehensive picture of the public's view of Bring Me the Horizon's song "Lost.".
Co-Authors ANANTA, ASTI Atuna, Annisa Salsabilah Kristofel Santa Kumajas, Sondy C. Sombo, Wanki Dwi Warsun Surbakti, Peliks Andreas Vivi Peggie Rantung