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Jurnal Informatika dan Rekayasa Perangkat Lunak
Published by Universitas Wahid Hasyim Semarang
ISSN : 26562855     EISSN : 26855518     DOI : http://dx.doi.org/10.36499/jinrpl
Core Subject : Science,
Computer Science & IT
Journal of Informatics and Software Engineering accepts scientific articles in the focus of Informatics. The scope can be: Software Engineering, Information Systems, Artificial Intelligence, Computer Based Learning, Computer Networking and Data Communication, and Multimedia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 222 Documents
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Implementasi Bussines Intelligence Untuk Menganalisis Perkembangan Akademik Mahasiswa Di Program Studi Sistem Informasi UNISNU Jepara Margaretha, Sintikhe Novia; Azizah, Noor; Sabilla, Alzena Dona
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i2.11312

Abstract

Penelitian ini bertujuan untuk membangun sistem pemantauan akademik berbasis Business Intelligence (BI) di Program Studi Sistem Informasi Unisnu Jepara guna meningkatkan efisiensi dan kualitas pengambilan keputusan. Metode yang digunakan melibatkan analisis data akademik mahasiswa dengan bantuan Microsoft Power BI untuk pengolahan data. Data diambil dari Sistem Informasi Akademik (SIAKAD) dan diolah untuk menghasilkan visualisasi dalam bentuk dashboard yang informatif. Hasil penelitian menunjukkan visualisasi data akademik yang mencakup jumlah mahasiswa, status akademik, IPK rata-rata, masa studi, dan keberhasilan lulusan. Dashboard ini memudahkan pemantauan dan analisis data akademik, mendukung pengambilan keputusan yang lebih baik, dan meningkatkan mutu pendidikan di Program Studi Sistem Informasi Unisnu Jepara. Penelitian  ini menyadarkan betapa pentingnya implementasi BI dalam mengoptimalkan manajemen data akademik dan pengambilan keputusan strategis di lingkungan akademik..
Identifikasi Kesegaran Ikan Bandeng Non-kontak Menggunakan MobileNetV2 Hidayatullah, Achmad Nasrul; Prasetyo, Eko; Purbaningtyas, Rani
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i2.10860

Abstract

Ikan bandeng merupakan komoditas yang diunggulkan di beberapa kabupaten di Indonesia yaitu Sidoarjo, Semarang, dan Banten. Ikan ini juga favorit masyarakat Indonesia karena tinggi gizi tinggi dengan harga terjangkau. Sehingga, bagi pebisnis hasil olahan ikan bandeng, kesegaran ikan bandeng menjadi parameter penting karena kesegaran ikan mempengaruhi kualitas hasil produk olahannya. Penyortiran ikan secara manual menjadi tidak masalah ketika jumlah ikan sangat banyak, karena rawan terjadi kesalahan akibat kelelahan. Selain itu, penyortiran ikan secara manual juga boros biaya dan waktu lama. Maka dari itu, dibutuhkan sistem otomatis non-kontak untuk mengindentifikasi kesegaran ikan berbasis citra digital. Penelitian ini bertujuan mengembangkan aplikasi identifikasi kesegaran ikan bandeng dengan menerapkan model Convolutional Neural Network (CNN). Kami menerapkan model MobileNetV2 untuk mengidentifikasi kesegaran ikan bandeng menjadi 3 kelas kesegaran yaitu sangat segar, segar, dan tidak segar. Pengujian aplikasi menggunakan model MobileNetV2 pada 312 citra ikan bandeng. Kinerja klasifikasi kesegaran mencapai 95 %, 70% dan 80% masing-masing pada kelas sangat segar, segar, dan tidak segar. Akurasi global sistem mencapai 81.6% menunjukkan bahwa aplikasi dapat bekerja dengan baik. Dari eksperimen dan analisis yang dilakukan, dapat disimpulkan bahwa sistem memiliki kemampuan baik dalam mengidentifikasi kesegaran ikan.
Metode System Development Life Cycle Dalam Perancangan Sistem Informasi Kuliah Kerja Kemasyarakatan Hidayat, Taufik; Sukisno, Sukisno; Nugroho, Asep Hardiyanto; Mubarrok, Abi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i2.11221

Abstract

Kegiatan tahunan yaitu Kuliah Kerja Kemasyarakatan dilaksanakan pada semester genap, yang dimana kegiatan tersebut dpat dilakukan dengan sistem pelaporannya masih mengirim gambar lewat whatsapp, Maka dari itu saya membuat sistem pelaporan itu sendiri sehingga dapat lebih efisien dalam pelaksanaannya. Mulai dari pelaporannya melalui website hingga monitorngnya juga melalui website. Maka dari itu peserta dan juga pembimbing dapat melaksanakan kegiatan dengan mudah. Metode yang digunakan adalah SDLC( System Development Life Cycle) sebagai penulisan, dan metode prototipe sebagai perancangan sistemnya. ISO9126 menjadi sistem testing dengan model funcionality dengan persentase 84% dan usability nya mendpatan 89,45%. Maka dari itu mulai dari testing, perancangan sistem hingga penulisan yang dilakukan sesuai kebutuhan.
Penentuan Penerima BSM Secara Objektiv Berdasarkan Metode Decision Support System VIKOR Tundo, Tundo; Akbar, Riolandi; Nugroho, Agung Yuliyanto; Saidah, Andi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

This research was conducted because of complaints from several parents regarding the BSM decision at SDN Kalanganyar ABC, however there were several students who were less well off because the choice of BSM was still subjective. SDN Kalanganyar ABC always holds activities related to BSM admissions once a year. It is hoped that this activity can also provide benefits for students who are poor but have excellent grades so they can carry out activities without being burdened by financial needs. In reality, there are still many students who do not receive BSM, even though according to the requirements, these students should be entitled to receive BSM. Therefore, there is a very irrational subjectivity in the ongoing elections. To overcome this problem, researchers tried to develop an application that applies the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, namely a method that makes decisions based on a rational compromise of criteria. These criteria include student reports, parents' income, academic achievement, dependents, home conditions, parents' relatives, and activity. From the results of the analysis and application of the VIKOR decision support system, subjective results were obtained for students whose evaluation standards and final decisions were lower than several other students, but the school provided BSM recommendations. To prevent the recurrence of this incident, VIKOR was able to answer objective findings with results of 76.57% with subjective findings of 23.43% in the previous system.
Klasterisasi Wilayah Kabupaten/Kota di Sulawesi Tenggara berdasarkan Produksi Bahan Pangan menggunakan Algoritma K-Means Clustering Mursawal, Mursawal; Saputra, Rizal Adi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Food ingredients are ingredients produced from agricultural products that are used to make food. These foods consist of vegetables, meat, nuts, sweet potatoes, and so on. Southeast Sulawesi Province is one of the provinces that has a fairly high amount of food production in Indonesia. The application of the K-Means clustering algorithm is used to group districts/cities in Southeast Sulawesi based on food production results. The method used in this study is CRISP-DM with the K-Means clustering algorithm. There are 17 districts/cities in Southeast Sulawesi used in this study. There are 7 types of food ingredients that will be used in the study, namely rice, corn, cassava, sweet potatoes, peanuts, soybeans, and green beans. The results of this study are 1 district/city that has a high level of food production, 4 districts/cities have a moderate level of food production, and 12 districts/cities have a low level of food production. The test results using the Davies Bouldin index are cluster 2 which has the best cluster quality because the results obtained from cluster 2 are 0.30, where the smaller the results obtained, the better the cluster.
Penerapan Algoritma Convolution Neural Network untuk Klasifikasi Jenis Cabai Berdasarkan Warna dan Bentuk buah Rohman, Rizal Abdur; Dasuki, Moh.; Muharom, Lutfi Ali; Rahman, Miftahur
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Chili is one of the main agricultural commodities in Indonesia with significant economic value. Various types of chili, such as large chili, bird's eye chili, and green chili, are often difficult to distinguish manually due to their physical similarities. To support advancements in the agricultural sector, this study utilizes artificial intelligence technology, specifically the Convolutional Neural Network (CNN) with VGG-16 architecture, to automatically identify chili types through image analysis based on color and shape. This study aims to measure the accuracy, sensitivity, and specificity of the model in classifying chili types. The results show that the VGG-16 architecture achieved 100% accuracy in training data testing, indicating the model’s ability to detect and classify chili types optimally. In the model evaluation (fold 5), the accuracy was 91.8%, sensitivity was 88%, and specificity was 93.8%. This study confirms that CNN with VGG-16 is effective for image classification, especially when test data shares similar characteristics with training data. This system offers significant potential for application in the agricultural sector, particularly in improving the efficiency and accuracy of identifying other agricultural commodities.
Analisis Implementasi SEO (Search Engine Optimization) dalam Kebutuhan Promosi Online pada Website Masteriwak.id Haris, Achmad; Dasuki, Mohammad; Arifianto, Deni
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Digital marketing is the main key in reaching a wider market in the digital era, especially through the use of websites. This research focuses on the implementation of Search Engine Optimization (SEO) to increase the visibility of the Masteriwak.id website, a platform engaged in online koi fish sales. This research uses a quantitative approach with the main stages being an initial performance audit, on-page and off-page SEO optimization, and evaluation of results using Google Search Console and SERPRobot. On-page SEO optimization involves improving internal elements such as meta title, meta description, and website speed, while off-page SEO focuses on strengthening backlinks from quality external sources. The implementation results show a significant increase in impressions, clicks, and keyword rankings on SERPs. For example, the keyword “where to sell koi fish nearby” rose from no rank to position 13 with 35 impressions and 4 clicks. Statistical evaluation using paired-samples t-test shows the difference before and after SEO optimization. The t-test values for the impression parameters were -1.641 (p = 0.243), clicks -2.524 (p = 0.128), and SERP rank -2.535 (p = 0.127), which showed improvement but not yet statistically significant. This study confirms the importance of systematically implementing SEO, although continuous optimization is still required for maximum results.
Prediksi Jumlah Penjualan melalui Live Stream dan Affiliate di TikTok Shop dengan Machine Learning Putri, Nabila Agustina Cahyani; Patrisiane, Viena
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Sales techniques have evolved rapidly from conventional methods to online methods often referred to as e-commerce. One of the types of e-commerce that exists in Indonesia is TikTok Shop, on TikToc Shop there are many sales supporting factors. The two sales supporting factors available on TikTok Shop are live stream and affiliate but there is no research to discuss the two factors simultaneously to the volume of sales. The aim of this study is to create a model to predict sales with live stream and affiliate factors on TikTok Shop. The method used in this research is by comparing the results of RMSE (Root Mean Squared Error) formed from the application of machine learning models multiple linear regression and random forest regression. The results of the study show that the best model based on RMSE for predicting sales with the live stream factor and the affiliate on TikTok shop is a multiple linear regression algorithm with RMSE(Root mean squared error) of 39.306882.
Pengelompokan Penyebaran Covid-19 di Provinsi Jawa Barat menggunakan Metode Clustering K-Medoids Luthfiyyah, Ibtihal Qomariyyah; Sari, Betha Nurina
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Covid-19 is a disease that infects the human respiratory system and has a high-speed transmission ability. West Java Province is one of the areas affected by the Covid-19 pandemic. The number of people confirmed with the Covid-19 virus in West Java is still increasing daily. Therefore, it is necessary to group the level of vulnerability to the spread of Covid-19, especially in West Java Province, using data from the official website of the West Java Provincial Government using 5 attributes, namely district_city_name, total_confirmation, confirmed_recovered, confirmed_death, and confirmed_active. This study aims to identify the pattern of the spread of Covid-19 to support more effective decision-making at the regional level. The research method involves a data mining process, namely business understanding, data understanding, data preparation, modeling, evaluation, and deployment based on the CRISP-DM methodology. The modeling process uses the K-Medoids algorithm with 3 clusters according to the government's color zone. The results of this study show 3 clusters, namely the green cluster is the minimum number of cases with 16 districts/cities. The yellow cluster is starting to be alert to the number of cases with 6 districts/cities. The red cluster is a very severe case with 5 districts/cities. The results of the Silhouette Coefficient test that tested n_cluster = 2, 3, 4, and 5 showed that n_cluster = 3 is the best cluster with a value of 0.77.
Klasterisasi Tukang Gigi di Jakarta dengan Algoritma K-Means berdasarkan Daerah Asal Zulkarnain, Iskandar; Fauziyah, Fauziyah
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Tukang gigi registered with the Indonesian tukang gigi Union (STGI) DPW DKI Jakarta organization are 625 members. STGI is an organization that has been incorporated by the Ministry of Law and Human Rights number AHU-000083. AH.01.08.Year 2020. In the distribution of tukang gigi in Jakarta consisting of various regions of origin, the researcher conducted clustering research to find out the distribution of the origin of Tukang Gigi in the Jakarta area. The scope of the research data area is: North Jakarta, West Jakarta, East Jakarta, South Jakarta and Central Jakarta. The results of the clustering of the K-Means Algorithm using the RapidMiner application are as follows: Cluster 0 (C0) : 1 Member, Cluster 1 (C1) : 16 Members, Cluster 2 (C2) : 1 Member. The results included in C0 are the Regency/City of origin of the most tukang gigi, namely: Pamekasan. The results included in C1 are the most diverse Regencies/Cities of the origin of tukang gigi, consisting of 16 Regencies/Cities, including the 3rd most Tukang Gigi' origin areas, namely: Jakarta. While those included in C2 are the 2nd most tukang gigi from origin, namely: Jember.

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