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Evaluasi Efektivitas Sistem Informasi Pengaduan Pelanggan PDAM Tirtanadi Cabang Medan Kota Berbasis Web Windi Imelda Putri; Aina Nur Nabila; Mhd. Furqan
Journal Of Informatics And Busisnes Vol. 1 No. 3 (2023): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v1i3.383

Penelitian ini berfokus pada evaluasi implementasi Sistem Informasi Pengaduan Pelanggan berbasis web di PDAM Tirtanadi cabang Medan Kota. PDAM sebagai penyedia layanan air menghadapi berbagai tantangan dalam menangani pengaduan pelanggan secara efisien. Studi ini mengungkapkan bahwa penerapan teknologi informasi dalam pengelolaan pengaduan pelanggan dapat menghasilkan manfaat yang signifikan. Sistem ini memungkinkan pelanggan untuk melaporkan masalah dengan lebih mudah dan cepat, sementara juga meningkatkan efisiensi operasional perusahaan. Kendati demikian, penelitian ini mengidentifikasi beberapa tantangan yang perlu diatasi, termasuk masalah keamanan data dan pelatihan karyawan. Hasil penelitian ini dapat menjadi pedoman bagi organisasi lain di sektor pelayanan publik yang berencana untuk mengadopsi atau meningkatkan sistem informasi pengaduan pelanggan mereka guna meningkatkan kualitas layanan.

Perancangan Sistem Kontrol Pendingin Udara Otomatis Berbasis Suhu Ruangan Menggunakan Arduino Khairi, Mhd Galih; Muhammad Irfan Gurning; Mhd. Furqan
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 3 No. 1 (2024): Januari 2024
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v3i1.96

Air conditioning has become essential in the modern era, with nearly everyone worldwide owning an air conditioner in their homes. Its presence is crucial given the increasingly hot weather conditions due to global warming. However, we often forget to turn off the air conditioner, resulting in energy waste and potential damage to the device. Therefore, this writing aims to address these issues through the development of an automatic system using Arduino Nano, programmed in the C language, to regulate the operation of the air conditioner based on room temperature. This way, users do not need to bother manually turning the air conditioner on or off using a remote control or buttons.

Application of the Naive Bayes Method for Determining the Quality of Crude Palm Oil (CPO) at PTPN 2 Sawit Seberang Prananda, Dimas Raka; Mhd. Furqan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4832

The palm oil industry is a vital pillar of Indonesia's economy, with Crude Palm Oil (CPO) as one of its leading commodities. The quality of CPO significantly impacts its competitiveness and market price internationally. PTPN 2 Sawit Seberang, as a prominent CPO processing company, faces challenges in consistently maintaining product quality. Key factors affecting CPO quality include moisture content, free fatty acids, and impurity levels, which are difficult to manage manually. To address these challenges, this study applies the Naive Bayes method as an efficient and fast classification tool for determining CPO quality. Naive Bayes was chosen for its simplicity in probability calculations and its ability to handle data classification with reasonable accuracy. The data used in this study include moisture content, free fatty acids, and impurity levels measured between February and June 2024. The data was split into training data (80%) and testing data (20%) and analyzed using RapidMiner software. The results show that the Naive Bayes method achieved an accuracy rate of 66.6%, with precision and recall values of 50% each. Although the accuracy could be improved, the application of this method has significantly enhanced the efficiency of determining CPO quality. Thus, the implementation of the Naive Bayes method in determining CPO quality at PTPN 2 Sawit Seberang is an effective step towards improving operational efficiency, classification accuracy, and decision-making quality related to product standards, ultimately supporting the company's competitiveness in the global market.

Klasifikasi Jenis Bunga Iris Menggunakan Algoritma Klasifikasi Tradisional Alwi Syahputra; Rusma Riansyah; Dimas Aqila Aptanta; Muhammad Farhan; Mhd. Furqan
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i2.1228

This study aims to implement and compare the performance of two traditional classification algorithms, namely K-Nearest Neighbor (K-NN) and Naive Bayes to classify Iris flower types. The dataset used is the Iris Dataset which is a classic dataset in machine learning consisting of 150 samples with four features (sepal length, sepal width, petal length, and petal width) and three target classes (Iris Setosa, Iris Versicolor, and Iris Virginica). The research methodology includes data preprocessing, algorithm implementation, model evaluation using accuracy, precision, recall, and F1-score metrics, and comparative performance analysis. The results showed that the K-NN algorithm with k = 3 achieved an accuracy of 96.67%, while Naive Bayes achieved an accuracy of 93.33%. Both algorithms showed good performance in classifying Iris flower types, with K-NN slightly superior in terms of accuracy. This study proves that traditional classification algorithms are still relevant and effective for classification problems with less complex datasets.

Analisis Hubungan Pengetahuan Dan Hygiene Sanitasi Toilet Masjid Dan Mushalla Di Kecamatan Pancur Batu Deli Serdang: Analysis of the Relationship between Knowledge and Hygiene and Sanitation of Mosque and Prayer Room Toilets in Pancur Batu District, Deli Serdang Alvi Nur Ilmi Br Ginting; Meutia Nanda; Mhd. Furqan
Jurnal Kolaboratif Sains Vol. 8 No. 10: Oktober 2025
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v8i10.8657

Masjid dan mushalla sebagai tempat ibadah yang digunakan oleh masyarakat secara luas, memerlukan fasilitas toilet yang memenuhi standar hygiene dan sanitasi guna mendukung kesehatan lingkungan dan kenyamanan jamaah. Penelitian ini bertujuan untuk menganalisis kondisi hygiene dan sanitasi toilet masjid dan mushalla di Kecamatan Pancur Batu, Deli Serdang. Penelitian ini menggunakan metode kuantitatif dengan pendekatan analitik dan melibatkan 60 toilet yang terdiri dari 50 masjid dan 10 mushalla dengan teknik total sampling. Data dikumpulkan melalui observasi dan kuesioner, kemudian dianalisis secara univariat dan bivariat menggunakan uji chi-square. Hasil penelitian menunjukkan bahwa sebanyak 70% toilet memenuhi standar hygiene dan sanitasi, sementara 30% toilet tidak memenuhi standar. Komponen toilet yang paling banyak tidak memenuhi syarat adalah fasilitas tempat sampah (81,7% tidak memenuhi syarat) dan ventilasi toilet (43,3% tidak memenuhi syarat). Hasil uji chi-square menunjukkan adanya hubungan yang signifikan antara pengetahuan pengelola toilet dengan kondisi hygiene dan sanitasi toilet (p = 0,000). Penelitian ini merekomendasikan peningkatan pengawasan dan edukasi kepada pengelola masjid dan mushalla untuk meningkatkan kualitas sanitasi toilet sesuai dengan standar yang berlaku.

Application of the Naive Bayes Method for Determining the Quality of Crude Palm Oil (CPO) at PTPN 2 Sawit Seberang Prananda, Dimas Raka; Mhd. Furqan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4832

The palm oil industry is a vital pillar of Indonesia's economy, with Crude Palm Oil (CPO) as one of its leading commodities. The quality of CPO significantly impacts its competitiveness and market price internationally. PTPN 2 Sawit Seberang, as a prominent CPO processing company, faces challenges in consistently maintaining product quality. Key factors affecting CPO quality include moisture content, free fatty acids, and impurity levels, which are difficult to manage manually. To address these challenges, this study applies the Naive Bayes method as an efficient and fast classification tool for determining CPO quality. Naive Bayes was chosen for its simplicity in probability calculations and its ability to handle data classification with reasonable accuracy. The data used in this study include moisture content, free fatty acids, and impurity levels measured between February and June 2024. The data was split into training data (80%) and testing data (20%) and analyzed using RapidMiner software. The results show that the Naive Bayes method achieved an accuracy rate of 66.6%, with precision and recall values of 50% each. Although the accuracy could be improved, the application of this method has significantly enhanced the efficiency of determining CPO quality. Thus, the implementation of the Naive Bayes method in determining CPO quality at PTPN 2 Sawit Seberang is an effective step towards improving operational efficiency, classification accuracy, and decision-making quality related to product standards, ultimately supporting the company's competitiveness in the global market.

Analisis Sentimen Terkait Kasus Korupsi Timah Pada Aplikasi X Menggunakan Algoritma Support Vector Machine Hasibuan, Suci Adina Ramadani; Mhd. Furqan
CESS (Journal of Computer Engineering, System and Science) Vol. 10 No. 2 (2025): Juli 2025
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v10i2.66522

Kasus dugaan korupsi yang melibatkan PT Timah Tbk dengan estimasi kerugian negara sebesar Rp 271 triliun telah memicu respons luas dari masyarakat, khususnya melalui media sosial X (dahulu Twitter). Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap kasus tersebut menggunakan algoritma Support Vector Machine (SVM). Data diperoleh dari platform X menggunakan kata kunci tertentu, menghasilkan 107 tweet yang kemudian melalui proses pra-pemrosesan dan pelabelan otomatis dengan metode berbasis leksikon (lexicon-based). Proses klasifikasi sentimen dilakukan dengan algoritma SVM, sementara ekstraksi fitur dilakukan menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF). Hasil evaluasi menunjukkan bahwa model mencapai akurasi sebesar 98%, dengan precision 98% dan recall 100% untuk sentimen negatif, serta precision 100% dan recall 60% untuk sentimen positif. Berdasarkan confusion matrix, sebesar 95,33% data diklasifikasikan sebagai negatif secara benar (True Negative), 2,80% sebagai positif secara benar (True Positive), dan 1,87% salah diklasifikasikan (False Negative), tanpa terdapat kesalahan klasifikasi positif palsu (False Positive). Temuan ini menunjukkan dominasi opini negatif terhadap kasus tersebut dan menegaskan efektivitas SVM dalam analisis sentimen publik. Penelitian ini diharapkan menjadi acuan dalam memahami persepsi masyarakat dan pengambilan keputusan berbasis data terhadap isu sosial-politik di Indonesia.

Algoritma K-Means Clustering untuk Mendiagnosis Bullying pada Remaja Di SMPN 1 Kecamatan Dolok Sigompulon Ritonga, Abdul Rois; Mhd. Furqan
CESS (Journal of Computer Engineering, System and Science) Vol. 10 No. 2 (2025): Juli 2025
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v10i2.66539

Bullying merupakan bentuk penyalahgunaan kekuasaan yang dapat terjadi di berbagai lingkungan, terutama di lingkungan sekolah. Tindakan ini mencakup perilaku seperti mengejek, mengancam, mengisolasi, hingga melakukan kekerasan fisik yang berdampak negatif pada kondisi psikologis korban. Di SMP N 1 Dolok Sigompulon, kasus bullying teridentifikasi dalam tiga bentuk, yaitu fisik, verbal, dan psikologis. Meskipun jumlah kasus tergolong rendah, fenomena ini tetap memerlukan perhatian serius karena dapat mengganggu kenyamanan belajar dan perkembangan sosial siswa. Untuk memahami pola dan karakteristik dari kasus bullying yang terjadi, diperlukan analisis data yang tepat. Salah satu metode yang dapat digunakan adalah K-Means Clustering, yaitu algoritma pengelompokan data yang membagi objek ke dalam beberapa klaster berdasarkan kemiripan karakteristik. Melalui metode ini, data bullying dapat diklasifikasikan menjadi beberapa kelompok, sehingga pola perilaku bullying di sekolah dapat dianalisis lebih mendalam dan menjadi dasar dalam pengambilan keputusan pencegahan. Berdasarkan hasil pengelompokan kasus bullying di SMP N 1 Dolok Sigompulon menggunakan algoritma K-Means maka di peroleh 3 cluster Dimana cluster 1 terdapat 12 data, cluster 2 terdapat 5 data, dan cluster 3 dengan 4 data sehingga dapat disimpulkan bahwa kasus bullying di SMP N 1 Dolok Sigompulon tergolong rendah dengan jenis bullying fisik, verbal, dan psikologis.

Classification Of Rice Plant Diseases Using K-Nearest Neighbor Algorithm Based On Hue Saturation Value Color Extraction And Gray Level Co-Occurrence Matrix Features Siti Saniah; Mhd. Furqan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3972

This research aims to classify diseases in rice plants using the K-Nearest Neighbor (K-NN) algorithm based on Hue Saturation Value (HSV) color feature extraction and Gray Level Co-Occurrence Matrix (GLCM) texture. The main problem faced is how to identify the type of disease in rice plants automatically using digital images. Diseases such as Blight, Tungro, and Crackle often attack rice plants and require an accurate early detection system. Lack of understanding in recognizing disease symptoms manually often leads to errors in handling. For this reason, this research develops an image processing-based classification system that can detect diseases such as Blight, Tungro, and Crackle. The method used in this research is image processing which includes RGB to HSV color space conversion, texture feature extraction using GLCM, and classification using K-NN algorithm. The dataset consists of 240 images, divided into training data and testing data, namely 192 training data and 48 testing data. Tests were conducted by calculating accuracy at various values of the K parameter, namely K = 1, K = 3, and K = 5, to determine the effectiveness of the model in classifying plant diseases. The purpose of this study was to evaluate the accuracy of the system in identifying rice diseases and test the combination of HSV and GLCM features in improving classification performance. The results showed that using HSV and GLCM features together resulted in the highest accuracy at K=3 with an accuracy value of 75%. The system is expected to assist farmers in detecting plant diseases quickly and effectively, thus minimizing production losses and supporting agricultural sustainability

Sentiment Analysis of Loudspeaker Regulations in Houses of Worship on Social Media Using Support Vector Machine Algorithm Manihuruk , Selly Novia; Mhd. Furqan; Lubis , Aidil Halim
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4043

Social media is an online platform where users can share content or interact with each other through discussions and debates that involve sentiments, such as agreement or disagreement on various topics. User sentiments on social media can be utilized in multiple ways, such as to gauge public opinion regarding the issuance of Circular Letter Number SE 05 of 2022 by the Ministry of Religious Affairs, which provides guidelines for the use of loudspeakers in mosques and prayer rooms. Due to the high volume of comments on social media regarding this circular, a sentiment analysis system is necessary. The sentiment analysis system in this research employs the Support Vector Machine (SVM) algorithm to classify comments as positive or negative. A total of 350 comments were collected from each social media platform—Facebook, Twitter, YouTube, and Instagram—about the issuance of the circular. These comments were divided into 250 for training data and 100 for testing data on each platform. The training data from all platforms were combined, resulting in a total of 1000 training data. Based on system testing using the Support Vector Machine algorithm, the accuracy achieved was 72%. This result reflects the system's capability to analyze sentiments related to the guidelines for using loudspeakers in mosques and prayer rooms as stated in the circular

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