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ANALISIS APLIKASI PENGAJUAN SURAT KETERANGAN PENDAMPING IJAZAH (APP-SKPI) MENGGUNAKAN ISO/IEC 25010 Asis, Muhammad Arfah; Ilmawan, Lutfi Budi; jeffry; Aziz, Firman; Usman, Syahrul; Fuadi Syam, Rahmat
Journal Pharmacy and Application of Computer Sciences Vol. 1 No. 2: Agustus: 2023: JOPACS
Publisher : Arlisaka Madani Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59823/jopacs.v1i2.27

Abstract

Penerapan Surat Keterangan Pendamping Ijazah (SKPI) atau diploma supplement merupakan amanat kurikulum berdasarkan Kerangka Kualifikasi Nasional Indonesia (KKNI) bagi setiap calon sarjana baru atau lulusan perguruan tinggi. SKPI memuat informasi prestasi dan kegiatan mahasiswa selama menjadi mahasiswa aktif di perguruan tinggi. Program studi Farmasi mengembangkan aplikasi untuk mengajukan SKPI yang disebut App-SKPI. Untuk membantu pengembangan aplikasi, telah dilakukan evaluasi dengan menggunakan model ISO 25010 untuk lima jenis kategori yaitu Functional Suitability, Performance Efficiency, Usability, Portability, dan Maintainability. Hasil pada kategori Functional Suitability, semua proses pada setiap fitur berjalan dengan baik dengan nilai 1 atau maksimal. Performance Efficiency, hasil kinerja dan struktur pada aplikasi mendapatkan Grade B dengan nilai kinerja 89% dan nilai struktural 91%. Usability, tingkat kepuasan mahasiswa terhadap sistem adalah 0,83. Portability, kemampuan adaptasi sistem pada browser yang berbeda mendapat nilai 1 atau maksimal. Maintainability, aplikasi dikembangkan dengan framework yang mendukung kemudahan perawatan
Implementasi Bridging Antrean Online V2 (Antrol) BPJS Kesehatan Pada Aplikasi SIMRS Khanza Zulfiana H, Yuli; Lokapitasari B, Poetri Lestari; Ilmawan, Lutfi Budi
Mutiara: Multidiciplinary Scientifict Journal Vol. 2 No. 2 (2024): Mutiara: Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v2i2.151

Abstract

Implementasi Bridgigng Antrean Online V2 (Antrol) bertujuan untuk mengetahui cara mengatasi penumpukan antrean di loket admission rumah sakit agar tidak terjadi rangkap data, mengetahui pengembangan web service antrean online versi 2.0 dengan aplikasi SIMRS Khanza dengan metode web service atau API (Application Programing Interface) agar dapat melakukan dua proses pelayanan tanpa ada intervensi satu sistem dengan sistem lainya secara langsung. Tahapan pengujian system menggunakan metode user acceptance test untuk melakukan validasi terhadap sistem yang dikembangkan berdasakan skenario. Dari hasil analisa data yang diperoleh dari hasil kuesioner yang diberikan kepada pengguna bahwa sistem bridging yang dikembangkan dapat diterima dengan presentasi skor 85% (baik).
Perbandingan Kinerja Word Embedding dalam Analisis Sentimen Ulasan Pengguna Aplikasi Perjalanan Pahendra, Muhammad Agung Maugi; Anraeni, Siska; Ilmawan, Lutfi Budi
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.9681

Abstract

Traveloka, as one of the leading travel booking platforms, has achieved more than 50 million downloads on Google Play Store. This achievement shows the high interest and trust of users in the services offered. However, user reviews indicate that there are some issues with the app's performance and stability that need to be taken into account. This research compares the performance of the Word2Vec and ELMo word embedding methods using the BiLSTM model in sentiment analysis of Traveloka application reviews. The research results show that the BiLSTM model with Word2Vec has an accuracy of 76.13%, precision 75.22%, and F1-measure 76.58%, better than the model with ELMo which has an accuracy of 74.38%, precision 70.49%, and F1-measure 74.40%. The BiLSTM model with Word2Vec is more effective in sentiment analysis of Traveloka reviews, helping identify and address user issues to improve service quality and user satisfaction.
Penerapan Website Sebagai Sarana Pendukung Pendidikan Pada Sekolah Luar Biasa (SLB) Autis Bunda Indra, Dolly; Ilmawan, Lutfi Budi
Jurnal BALIRESO Vol 9, No 2 (2024)
Publisher : Lembaga Pengabdian kepada Masyarakat Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/balireso.v9i2.333

Abstract

Identification of Brain Areas Associated with Chronic Neuropathic Pain through Hjorth Parameter Analysis of EEG Signals Asyrafi, Hilman; Handayani, Nita; Ilmawan, Lutfi Budi; Shabir, Fadly; Jamal, Ridwan; Jannah, Miftahul; Arysespajayadi
Jurnal Fisika Vol. 15 No. 2 (2025): Jurnal Fisika 15 (2) 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jf.v15i2.24512

Abstract

This study investigates the neurophysiological signatures of chronic neuropathic pain (CNP) through the analysis of EEG signals using Hjorth parameters (Activity, Mobility, and Complexity). We compared EEG recordings from 36 CNP patients with those from 19 healthy controls (HC) under both eyes-open and eyes-closed conditions. Analysis of 19 electrode locations revealed significant differences between the groups across all Hjorth parameters. The Activity parameter showed dramatic elevations in CNP patients across all brain regions, indicating widespread cortical hyperexcitability. Mobility parameters revealed significant alterations particularly in occipital (O2), central midline (Cz), and parietal (Pz) regions, with strong effect sizes (Cliff's delta > 0.7). Complexity parameters demonstrated significant changes in right temporal (T4) and parietal midline (Pz) areas. The combined analysis identified the parietal cortex, temporal regions, occipital cortex, and central midline as key areas associated with CNP, suggesting a distributed network disruption rather than localized dysfunction. These findings contribute to our understanding of the neural mechanisms underlying chronic neuropathic pain and may support the development of objective diagnostic markers and targeted interventions for this debilitating condition.
Analisis Kinerja QoS (Quality of Service) Jaringan WLAN Ukhuwahnet Pada Universitas Muslim Indonesia Alwi, Erick Irawadi; Ilmawan, Lutfi Budi
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 3 No. 1 (2019): PROSIDING SEMNAS INOTEK Ke-III Tahun 2019
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v3i1.513

Abstract

Universitas Muslim Indonesia merupakan sebuah instansi di bidang pendidikan yang menggunakan jaringan internet sebagai salah satu penunjang sarana dan prasana untuk mengelola dan mengatur data informasi serta digunakan mahasiswa untuk mencari referens mengenai perkuliahan mereka. Universitas Muslim Indonesia memiliki 13 fakultas, dimana setiap fakultas menggunakan jaringa wireless LAN sebagai sarana untuk mahasiswa dalam menggunakan jaringan internet, kelemahan dari jaringan wireless LAN dimana buruknya jaringan internet pada jaringan wireless LAN. Maka dari itu peneliti ingin menganalisis Quality of Service (QoS) jaringan wireless LAN pada setiap fakultas di lingkup Universitas Muslim Indonesia. Dengan adanya kualitas jaringan pada Universitas Muslim Indonesia maka akan dilakukan metode wawancara dan observasi terlebih dahulu mengenai masalah yang terjadi pada jaringan wireless LAN pada tiap fakultas, setelah itu akan dilakukan analisis terhadap jaringan wireless LAN dengan menggunakan parameter - parameter Quality of Service yaitu delay, packet loss, bandwidth, troughput.
Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store Ilmawan, Lutfi Budi; Mude, Muhammad Aliyazid
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.597.154-161

Abstract

In this research, the performance of SVM classification method will be compared with other classification methods, by using the Naïve Bayes classification method. Naïve Bayes classification method is a light classification method and has a high accuracy if applied to the text classification according to some previous studies. The accuracy of the classifier is measured using the K-fold cross validation method whose results will be tabulated in a confusion matrix table, with a value of K = 3. In this study, the data processed are textual reviews of applications in the Indonesian language Google Play Store obtained from previous research. The test results obtained from the 3-fold cross-validation method produce that SVM Classifier has a higher value of accuracy when compared with the accuracy of the Naïve Bayes classifier, the SVM classifier gets an accuracy of 81.46% and Naïve Bayes classifier by 75.41%.
MEMBANGUN WEB CRAWLER BERBASIS WEB SERVICE UNTUK DATA CRAWLING PADA WEBSITE GOOGLE PLAY STORE Ilmawan, Lutfi Budi
ILKOM Jurnal Ilmiah Vol 10, No 2 (2018)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v10i2.282.215-224

Abstract

At this time, Google Play Store is not providing API that can be used for accessing datas from applications on it’s application store. With that plenty application’s data, it could be used to make it a good research object, specially on data mining field. In this research, the system that is built is the system that can retrieve that applications’ data. For multiplatform’s purpose, web services are used for being an interface between client and server. Finally, the built system is working as expected. The system can retrive data from Google Play Store and it is suitable from requirements of data analysis stage. It can also integrated with REST web service to provide multiplatform access.
Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter Mukarramah, Rifqatul; Atmajaya, Dedy; Ilmawan, Lutfi Budi
ILKOM Jurnal Ilmiah Vol 13, No 2 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i2.851.168-174

Abstract

Sentiment analysis is a technique to extract information of ones perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify societys response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.