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All Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTI Nusa Mandiri Amalee: Indonesian Journal of Community Research & Engagement Jurnal Sistem Komputer dan Informatika (JSON) International Journal of Engineering, Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Decode: Jurnal Pendidikan Teknologi Informasi Jurnal Teras Pengabdian Masyarakat Scientific: Journal of Computer Science and Informatics
Afril Efan Pajri
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Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Sistem Cerdas Deteksi Asap Rokok Secara Real-Time Berbasis Sensor MQ-2 dan Mikrokontroler ATMEGA 16 untuk Keamanan Ruangan Sutriawan, Sutriawan; Afril Efan Pajri; Taufik Azhary
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 8 No. 1 (2025): J-SISKO TECH EDISI JANUARI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v8i1.10604

Abstract

Asap rokok sangat berbahaya bagi kesehatan manusia karena mengandung zat yang mematikan, seperti nikotin salah satunya. Walaupun asap rokok ini berbahaya,kebutuhan akan rokok tidak dapat dihentikan karena besarnya permintaan konsumen dan kebanyakan orang tidak peduli akan efek negatif asap rokok. Banyak cara yang dilakukan untuk meminimalisir bahaya asap rokok bagi perokok pasif, salah satunya dengan cara membuat stiker atau pun larangan untuk tidak merokok. Namun ternyata cara ini kurang efektif karena masih ada orang yang merokok walaupun sudah mengetahui larangan tersebut. maka penelitian ini bagaimana membuat sistem detektor asap menggunakan sensor MQ-2 berbasis mikrokontroler Atmega 16. Tujuan dari penelitian ini adalah membangun sebuah alat untuk mendeteksi asap sehingga jika asap semakin menggumpal dan banyak maka akan muncul informsi busher. sumber kaidah, konsep dan teori-teori yang mendukung dalam penyelsaian masalah dalam penelitian ini. Pada tujuan ini ditentukan tujuan yang akan dicapai, terutama dalam mengatasi masalah yang ada
Tensile and Flexural Properties of Epoxy Nanocomposites Reinforced with Cellulose Nanocrystals Azhary, Taufik; Pajri, Afril Efan
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.892

Abstract

Composite materials are extensively utilized across various fields such as engineering, aviation, automotive, construction, and healthcare. This widespread application highlights their superior properties, often absent in the individual constituent materials. Additionally, composites offer the advantage of being easily fabricated to meet specific requirements, and incorporating natural fibers as reinforcement has gained significant interest due to their environmental friendliness and abundance. Among these, nanocellulose is a promising green material due to its unique characteristics. Specifically, cellulose nanocrystals (CNC), nanoscale derivatives of nanocellulose, have attracted considerable attention as reinforcement agents in composite fabrication. This interest stems from CNC's notable advantages, including excellent mechanical properties, a high crystallinity index, plentiful availability, low weight, and eco-friendly nature. This study was undertaken to investigate the impact of varying concentrations of cellulose nanocrystal (CNC) (0, 0.5, 0.75, 1 wt%) on the mechanical properties, specifically the tensile and flexural properties, of epoxy resin/cellulose nanocrystal (E/CNC) nanocomposites. The materials employed in this research include epoxy resin, hardener, and cellulose nanocrystals. The fabrication of the E/CNC nanocomposites was carried out through a straightforward mixing method, wherein the constituent materials were blended following the defined experimental parameters, followed by the molding process. The findings of this study indicate that the incorporation of cellulose nanocrystals (CNC) significantly enhances the mechanical properties of E/CNC nanocomposites. The E/0.75CNC nanocomposite showed optimal tensile strength (39.91 MPa; +4.95%), while E/1CNC exhibited superior flexural strength (65.78 MPa; +5.08%) compared to the unmodified epoxy baseline.
RANCANG BANGUN APLIKASI PREDIKSI TAGIHAN AIR BERBASIS WEB MENGGUNAKAN REGRESI LINIER BERGANDA Arum , Arum Fatmawati; Pajri, Afril Efan; Sahri
INTI Nusa Mandiri Vol. 20 No. 1 (2025): INTI Periode Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.6899

Abstract

The management of water billing in the PAMSIMAS service in Sidobandung Village is still conducted manually and does not provide early information regarding bill estimates, often resulting in delayed payments by customers. This study aims to design and develop a web-based water bill prediction application using the Multiple Linear Regression (MLR) method, capable of delivering fast, accurate, and accessible billing estimates. The dataset used in this research consists of historical monthly water usage and billing data from January to December 2024, with a structure comprising 231 rows of customer data and 30 feature columns. The research stages include data preprocessing, model training using MLR, integration of the model into a web-based system, and evaluation of prediction results using the Mean Squared Error (MSE) and R-squared ( ) metrics. Evaluation results showed that the model achieved an MSE of 18,882 and an  of 0,8, indicating a fairly good and stable prediction performance. The system allows customers to log in, view predicted water bills for the 13th month based on previous data, and access graphical visualizations of usage and cost trends. Meanwhile, the admin can efficiently manage customer data through a dedicated dashboard. With the implementation of this application, the management and prediction process of water billing becomes more transparent, efficient, and helps customers in planning their water expenses more precisely .
Klasisifikasi Tingkat Kemiskinan Di Indonesia Menggunakan Algoritma Naives Bayes Suci Mulyani; Pajri, Afril Efan; Fikram, Muhammad
Scientific: Journal of Computer Science and Informatics Vol. 1 No. 2 (2024): Juli 2024
Publisher : Universitas Muhammadiyah Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34304/scientific.v1i2.333

Abstract

Abstrak penelitian ini dilatarbelakangi oleh urgensi klasifikasi tingkat kemiskinan yang akurat sebagai landasan pengambilan keputusan dan penyaluran bantuan yang tepat sasaran. Penelitian ini bertujuan untuk membangun model klasifikasi tingkat kemiskinan menggunakan algoritma Naïve Bayes yang diimplementasikan dalam software RapidMiner. Metode penelitian yang digunakan meliputi pengumpulan data dari berbagai sumber, pra-pemrosesan data untuk membersihkan dan mentransformasi data, implementasi algoritma Naïve Bayes untuk membangun model klasifikasi, serta evaluasi kinerja model menggunakan metrik akurasi, presisi, dan recall. Hasil penelitian menunjukkan bahwa model yang dibangun mencapai akurasi sebesar 96.12%, dengan kemampuan identifikasi yang baik pada kelompok tidak miskin (presisi 100%) dan kelompok miskin (recall 100%). Meskipun demikian, presisi pada kelompok miskin masih perlu ditingkatkan (75%) untuk meminimalkan kesalahan klasifikasi dan memastikan bantuan yang diberikan lebih efektif dan tepat sasaran.
Penguatan Kualitas Produk, Pengelolaan, dan Pemasaran Sirup Sapodilla 'Putri' Shoimah, Siti; Afril Efan Pajri; Fatichatur Rochmaniyah; Eka Fadlilatus Saadah; Muhammad Sholihin
Amalee: Indonesian Journal of Community Research and Engagement Vol. 6 No. 2 (2025): Amalee: Indonesian Journal of Community Research and Engagement
Publisher : LP2M INSURI Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/amalee.v6i2.8124

Abstract

This community service program was carried out to empower the Home Industry (IRT) of “Putri” Sapodilla Syrup in Pangkatrejo Village, Sugio Subdistrict, Lamongan. The partner faced challenges in three main aspects: inconsistent product quality and a short shelf life due to limited production technology, weak business management systems and financial record-keeping, and a marketing strategy limited to conventional promotional methods. The implementation method consisted of five stages: socialization, training, application of appropriate technology, mentoring, and evaluation. Interventions included the use of pasteurization machines, heavy-duty blenders, pH meters, refractometers, and vacuum sealers to improve product hygiene and shelf life. In terms of management, training was provided on digital financial recording, brand identity development, and simple business archiving. In marketing, redesigned packaging was introduced through Canva Pro, the activation of social media accounts (Instagram and TikTok), and distribution via e-commerce platforms (Shopee). The results showed significant improvements: the syrup product can now last up to three months without preservatives, sales increased by up to 30% within six months, and the partner’s managerial skills improved through digital record-keeping. Specifically, the partner’s average monthly revenue increased from IDR 3,000,000 before the program to approximately IDR 4,000,000 - 4,200,000 after the interventions. This program contributes to the achievement of SDGs 8, 9, and 12, and supports the strengthening of a green economy based on local potential.  
Optimalisasi Manajemen Operasional dan Pemasaran Digital untuk Meningkatkan Daya Saing Produk Upcycling di BUMDes Berkaho Pungpungan Sahri, Sahri; Tawakkal, M. Iqbal; Pajri, Afril Efan
Jurnal SOLMA Vol. 14 No. 3 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i3.20263

Abstract

Background: Program menghadapi permasalahan rendahnya efisiensi produksi serta terbatasnya akses pasar pada produk upcycling BUMDesa Berkaho. Hal ini disebabkan kurangnya pemanfaatan teknologi dan strategi pemasaran yang belum optimal. Tujuan: Program ini bertujuan untuk meningkatkan daya saing produk upcycling di BUMDes Berkaho melalui optimalisasi manajemen operasional dan pemasaran digital. Metode: Metode yang digunakan berbasis Participatory Learning and Action (PLA) yang mencakup sosialisasi, pelatihan, penerapan teknologi, monitoring dan evaluasi, serta perencanaan keberlanjutan. Pelatihan difokuskan pada peningkatan keterampilan manajerial, penggunaan alat produksi tepat guna, serta pemanfaatan media sosial sebagai sarana promosi. Hasil: Hasil kegiatan menunjukkan adanya peningkatan kapasitas pengurus dan anggota BUMDesa dalam mengelola usaha, melakukan diversifikasi produk, dan terlibat aktif dalam pemasaran digital. Kesimpulan: Program ini berkontribusi dalam memberdayakan potensi lokal melalui pemanfaatan teknologi dan kolaborasi masyarakat, serta membangun fondasi usaha desa yang lebih berkelanjutan dan adaptif terhadap dinamika pasar.
Sentiment Analysis of President Prabowo's Performance on Twitter (X) with a Comparative Study of SVM, XGBoost, and AdaBoost Maruf, Anang; Pajri, Afril Efan; Liana, Putri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12138

Abstract

This study was conducted to understand how Twitter (X) users respond to President Prabowo's performance through machine learning-based sentiment analysis. Data was collected using a dataset crawling approach, then processed through a series of pre-processing stages such as cleansing, case folding, tokenisation, stopword removal, and stemming before being converted into a numerical representation with TF-IDF. The class imbalance problem was addressed by applying SMOTE so that the model could learn more evenly. Three classification algorithms, SVM, XGBoost, and AdaBoost, were tested with the help of GridSearchCV to obtain the best parameter configuration. The research evaluation showed that the XGBoost algorithm was able to provide the best performance with an accuracy of 0.8443, followed by the SVM algorithm with an RBF kernel, which achieved an accuracy of 0.8135. The AdaBoost algorithm came in third with an accuracy of 0.7868. These findings indicate that the boosting approach, especially XGBoost, is better able to handle complex language patterns and high-dimensional text data characteristics. Overall, this study provides an overview of public opinion trends on social media and can be used as a reference for the development of sentiment analysis models in future research.
Classification Of Student Depression Using Support Vector Machine Modelling and Backward Elimination Sabar, Rohmat Abidin; Pajri, Afril Efan; Budiani, Jauhara Rana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12203

Abstract

Depression among university students has become a serious mental health concern that can negatively affect academic performance and overall well-being. Early detection of Depression is essential to provide timely support and preventive interventions. This study proposes a machine learning approach to classify student Depression using a Support Vector Machine (SVM) combined with Backward Elimination (BE) for feature selection. The dataset used in this research was obtained from a public repository and consists of 502 student records with multiple psychological and demographic attributes. Data preprocessing included categorical encoding and Min–Max normalization, followed by an 80:20 split for training and testing. Experimental results show that the baseline SVM model achieved an accuracy of 0.9208, while the application of Backward Elimination improved the performance to 0.9604. In addition, precision, recall, and F1-score also showed notable improvements, indicating a reduction in misclassification, particularly for non-depressed students. These findings demonstrate that integrating feature selection with SVM can enhance classification performance and provide a more efficient model for supporting early Depression detection among university students.
Analisis Sentimen Komentar iPhone 17 pada Platform YouTube Menggunakan IndoBERT dan Support Vector Machine MARATUS SHOLIHAH, SITI; Afril Efan Pajri; Ita Aristia Sa’ida
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9507

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen komentar YouTube berbahasa Indonesia terkait iPhone 17 dengan membandingkan metode Support Vector Machine berbasis Term Frequency–Inverse Document Frequency dan model IndoBERT. Data diperoleh melalui proses crawling komentar pada kanal YouTube GadgetIn, kemudian diproses melalui tahapan pre-processing untuk mengurangi noise dan menormalkan teks. Pelabelan sentimen dilakukan secara otomatis menggunakan InSetLexicon dengan dua kelas, yaitu positif dan negatif. Dataset selanjutnya dibagi menggunakan teknik stratified split menjadi data latih, validasi, dan uji. Selain dua model utama, pendekatan ensemble IndoBERT–SVM diuji sebagai metode tambahan untuk menilai stabilitas performa klasifikasi. Evaluasi dilakukan menggunakan confusion matrix serta metrik Accuracy, Precision, Recall, dan F1-score. Hasil pengujian menunjukkan bahwa IndoBERT memperoleh performa terbaik dengan nilai Accuracy sebesar  92, 29%, diikuti oleh model ensemble sebesar 91,63%, dan Support Vector Machine sebesar 88,99%. Temuan ini mengindikasikan bahwa model berbasis transformer lebih efektif dalam memahami konteks bahasa informal pada komentar YouTube dibandingkan metode berbasis fitur tradisional. Dengan demikian, penelitian ini memberikan bukti empiris mengenai efektivitas pendekatan machine learning dan transformer dalam analisis sentimen media sosial berbahasa Indonesia.
Analisis Sentimen Pemindahan Ibu Kota Nusantara (IKN) dengan Algoritma Random Forest, Naive Bayes, dan Support Vector Machine Aini, Siti Azizah; Pajri, Afril Efan; Kartini, Alif Yuanita
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 1: MARET 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i1.1567

Abstract

Reaksi masyarakat Indonesia terhadap pemindahan Ibu Kota Nusantara (IKN) bervariasi, terutama di media sosial, yang merupakan platform utama untuk menyuarakan pendapat. Tujuan penelitian ini adalah untuk mengevaluasi persepsi publik tentang rencana pemindahan IKN dengan menggunakan tiga metode klasifikasi yaitu Random Forest, Naïve Bayes, dan Support Vector Machine (SVM). Data diproses melalui beberapa tahap preprocessing, Tahap-tahap ini termasuk tokenisasi, penghilangan kata umum, pemrosesan huruf, pembersihan teks, dan pengurangan kata dasar. Pelabelan data mengelompokkan komentar dalam kategori sentimen positif dan negatif, sedangkan ekstraksi fitur dilakukan dengan metode TF-IDF. Dataset tersebut kemudian dibagi menjadi dua, 20% untuk data pengujian dan 80% untuk data latihan. Hasil penelitian ini menunjukkan bahwa setiap model memiliki tingkat akurasi yang berbeda, dengan Random Forest mencapai 98%, Naïve Bayes 91%, dan SVM 97%, yang menunjukkan bahwa Random Forest adalah algoritma ensemble yang paling akurat dan dapat digunakan untuk analisis sentimen.Studi ini menemukan bahwa setiap model memberikan tingkat akurasi yang berbeda. Hasil penelitian ini menunjukkan bahwa Random Forest adalah algoritma ensemble terbaik untuk mengelompokkan sentimen publik tentang masalah pemindahan IKN. Kesimpulan penelitian ini menekankan bahwa menggunakan algoritma ensemble seperti Random Forest dapat menghasilkan analisis sentimen yang lebih akurat, dan ini dapat digunakan sebagai pedoman untuk proses pengambilan keputusan di masa mendatang tentang evaluasi kebijakan publik
Co-Authors Aini, Siti Azizah Arum , Arum Fatmawati Denny Nurdiansyah Eka Fadlilatus Saadah Fatichatur Rochmaniyah Fikram, Muhammad Ita Aristia Sa'ida Jauhara Rana Budiani, Jauhara Rana Kartini, Alif Yuanita Liana, Putri M. Iqbal Tawakkal MARATUS SHOLIHAH, SITI Maruf, Anang Meilasaroh, Wella Vernanda Muhammad Sholihin Sabar, Rohmat Abidin Sahri Sahri Sahri Sirozul Huda Siti Shoimah Suci Mulyani Sutriawan Taufik Azhary Tawakkal, M. Iqbal