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                <text>Penetration Testing pada Kerentanan Keamanan Sistem PELAKAT Menggunakan SQL Injection</text>
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                <text>Penetration Testing, SQL Injection, Burp Suite, SQLMap, Kerentanan Sistem</text>
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                <text>Penetration Testing bertujuan untuk mengidentifikasi kerentanan sistem dengan cara mensimulasikan serangan dengan teknik tertentu seperti SQL Injection. Sistem Pelayanan Administrasi Kependudukan yang Mendekatkan Masyarakat (PELAKAT) adalah sebuah aplikasi berbasis website yang dibuat oleh Dinas Kependudukan dan Pencatatan Sipil (Disdukcapil) Kabupaten Sambas untuk memudahkan proses pengelolaan beberapa dokumen administrasi kependudukan (Adminduk). Pengujian keamanan sistem PELAKAT menggunakan teknik SQL Injection diperlukan untuk mengidentifikasi kerentanannya serta memberikan rekomendasi mitigasi. Tahapan metode penetration testing yang dilakukan yaitu reconnaissance, scanning, vulnerability assessment, exploitation, dan reporting. Tools yang digunakan yaitu Burp Suite untuk menganalisis HTTP request dan SQLMap untuk eksploitasi kerentanan. Berdasarkan hasil pengujian, salah satu parameter pada form login sistem PELAKAT diketahui rentan terhadap SQL Injection. Eksploitasi berhasil mengakses sembilan database, lima tabel pada salah satu database, dan 13 kolom pada salah satu tabel. Kerentanan ini disebabkan karena sistem dikembangkan tanpa fitur keamanan yang memadai. Tingkat kerentanan sistem dinilai tinggi karena sistem PELAKAT dinyatakan rentan terhadap SQL Injection sehingga diperlukan tindakan mitigasi. Rekomendasi mitigasi meliputi penerapan WAF (Web Application Firewall), validasi input pada form input, penggunaan prepared statements, implementasi framework seperti Laravel, dan migrasi database ke penyimpanan berbasis cloud. Dengan penerapan mitigasi ini, diharapkan dapat meningkatkan keamanan sistem dan meminimalisir kerentanan sistem</text>
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                <text>Khairul a, Asrul Abdullah b,*, Sucipto </text>
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                <text>https://teknosi.fti.unand.ac.id/index.php/teknosi/issue/view/29</text>
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                <text>Universitas Muhammadiyah Pontianak</text>
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                <text>13 mei 2025</text>
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                <text>Fajar bagus W</text>
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                <text>Uji Kualitas Website Admin Travel Booking Menggunakan Halstead’s Metric dan Equivalence Partitioning</text>
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                <text>Testing, Halstead's Metric, Equivalence Partitioning, Travel System</text>
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                <text>Tripease is a travel booking admin website designed to enable admins with management roles for train tickets, hotel tickets, and user data. Blackbox testing ensures the software meets the set requirements  and  specifications  and  detects  functional  and  performance  errors.  Whitebox testing is done to check the software's logical path by examining the software's logical structure and  detecting  errors,  such  as  logical  errors  and  script  understanding  errors.  This  research discusses testing the travel booking admin website using two methods: Halstead's Metric and Equivalence Partitions. This research aims to determine the success rate of features and predict the appearance of bugs on the website. White box testing with Halstead's Metric shows that the add train feature has the highest prediction of bugs (45.7%), while the add user feature is the lowest (13.4%). The average prediction of the occurrence of bugs in all features is 25.59%. Black box testing with Equivalence Partitions shows that the add user and edit user features have a low success rate because they do not succeed in the input validity test item. The add train and login features have a 100% success rate. Based on the test results, it is recommended that the code be refactored and the input validity of each feature improve</text>
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                <text>Fauziah Nur Syifa a, Trisana Nurul Anzali Nabil b, Derrel Hendi Arifin c, Rangga Sidik</text>
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                <text>https://teknosi.fti.unand.ac.id/index.php/teknosi/issue/view/29</text>
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                <text>unikom</text>
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                <text>4 MEI 2025</text>
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                <text>Analisis Trend Penelitian Penggunaan Algoritma Penjadwalan serta Faktor yang Mempengaruhinya: Analisis Bibliometrik R dan Pemetaan VOSviewer</text>
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                <text>Algoritma penjadwalan, Bibliometrik R, Round Robin, Sistem operasi </text>
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                <text>Penjadwalan merupakan salah satu cara yang digunakan untuk menghindari masalah dalam sistem operasi. Penjadwalan memiliki berbagai algoritma diantaranya Round Robin, First in First out, dan SJF. Penelitian ini bertujuan untuk menganalisis tinjauan literatur trend penelitian algoritma penjadwalan serta faktor-faktor yang mempengaruhinya melalui analisis mapping bibliometrik R pada penelitian yang sudah dipublikasikan pada jurnal terindeks Scopus dan Google Scholar dari tahun 2019 sampai 2023. Metode yang digunakan dalam penelitian ini adalah analisis bibliometrik R.  Publish  or perish digunakan sebagai  aplikasi pencarian dan VOSviewer digunakan sebagai aplikasi pemetaan data. Hasil penelitian ini menunjukkan bahwa perkembangan jumlah publikasi penelitian mengenai Algoritma Penjadwalan yang terindeks google scholar dari tahun 2019 – 2023 mengalami penurunan secara siginifikan (383 (2019), 314 (2020), 195 (2021), 84 (2022), dan 20 (2023)). Sedangkan berdasarkan database Scopus, perkembangan  penelitian  mengenenai  algoritma  penjadwalan  mengalami  fluktuasi  yaitu penurunan pada tahun 2021 (200 artikel) dan peningkatan pada tahun 2022 (830 artikel). Pada tahun  dari tahun 2020 mengalami penurunan  yang  signifikan.  Berdasarkan hasil pemetaan VOSviewer didapatkan bahwa bahwa algoritma penjadwalan proses yang banyak digunakan untuk diteliti adalah algoritma Round Robin (RR) yang berada di cluster 1, cluster 3, dan cluster 7. Alasan algoritma RR banyak diminati karena mampu memberikan keadilan dalam pembagian waktu pemrosesan CPU diantara proses-proses yang berjalan</text>
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                <text>Dwi Novia Al Husaeni a,*, Jajang Kusnendar a</text>
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                <text>https://teknosi.fti.unand.ac.id/index.php/teknosi/issue/view/29</text>
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                <text>Universitas Pendidikan Indonesia</text>
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                <text>30 april 2025</text>
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                <text>Fajar bagus W</text>
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                  <text>VOL 11 NO 1 (2025)</text>
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                <text>Penerapan  Metode  Clustering  Pada  Penjualan  Makanan  Dan  Minuman  Dalam Menentukan Strategi Marketing</text>
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            <description>The topic of the resource</description>
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                <text>Clustering, Data Mining, Data Penjualan, K-Means, Strategi Marketing</text>
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                <text>Persaingan  bisnis  termasuk  bisnis  restoran  semakin  ketat  dengan  adanya  perubahan  cara berbisnis dari konvensional menjadi online (digital). Hal yang menjadi permasalahan ialah menu yang terjual tidak selalu sebanding dengan pengeluaran bahan baku yang dibeli restoran untuk  membuat  menu  makanan  dan  minuman  mereka.  Oleh  sebab  itu,  diperlukan  strategi marketing yang sesuai untuk dapat diterapkan. Restoran yang dijadikan bahan penelitian adalah restoran “Ben’s Haus Bistro”. Atribut yang digunakan terdiri dari Item Name, Category Name, dan Item Sold.  Pada  penelitian  ini  akan menerapkan salah  satu metode data mining,  yaitu metode clustering  menggunakan algoritma K-Means  untuk  menentukan  strategi  marketing.  Selain itu juga akan dilihat berapa tingkat akurasi yang didapatkan dari hasil proses clusteringyang  telah  dilakukan.  Berdasarkan  hasil  tingkat  akurasi  dari  proses clustering  yang  telah dilakukan,  didapatkan  nilai  sebesar  0,6595.  Sedangkan  hasil  penerapan  metode clusteringmenggunakan algoritma K-Means untuk data penjualan secara keseluruhan menghasilkan 4 cluster, karena hasil perhitungan metode elbow untuk menentukan nilai K optimal mendapatkan hasil K=4. Cluster 1 memperoleh hasil sebanyak 6 item, Cluster 2 memperoleh hasil sebanyak 10  item, Cluster  3  memperoleh  hasil  sebanyak  43  item,  dan Cluster  4  memperoleh  hasil sebanyak 52 item. Dari hasil penelitian yang telah dilakukan, dapat disimpulkan bahwa metode clustering menggunakan algoritma K-Means dapat diterapkan pada data penjualan makanan dan minuman untuk membantu dalam menentukan strategi marketing</text>
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                <text>Eufrasia Paskasius a,, Evasaria Magdalena Sipayung </text>
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                <text>ABizLSM:  Kerangka  Migrasi  Sistem  Lawas  Genting Arsitektur  Monolitik  ke Microservices  untuk Perusahaan Startup dengan Dinamika Bisnis Tinggi</text>
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                <text>Proses rekayasa ulang migrasi sistem monolitik lama ke sistem berbasis microservices sering kali  penting  bagi  kelangsungan  bisnis.  Pada  ruang  lingkup  perusahaan startup,  dinamika perubahan bisnis cenderung lebih gesit dan cepat, yang membuat sistem sering kali tidak terawat  dan  terdokumentasi  dengan  baik.  Di  sisi  lain,  pengembang  ditantang  untuk memperbarui sistem lama dengan perubahan bisnis perusahaan startup yang dinamis dari waktu ke waktu. Banyak studi telah mengusulkan proses migrasi sistem monolitik lama. Namun, penelitian umumnya terfokus pada perusahaan korporat, yang sering tidak mempertimbangkan proses migrasi dalam  lingkungan bisnis perusahaan startup yang gesit. Dengan demikian, penelitian ini membahas masalah tersebut dan mengusulkan kerangka kerja Agile  Business Legacy System Migration (ABizLSM) yang berasal dari prinsip-prinsip Domain-Driven Design (DDD) dengan fokus pada migrasi sistem monolitik lama di perusahaan startup. Penelitian ini menggunakan metode penelitian campuran dengan melakukan wawancara dan mengevaluasi sistem yang dimigrasikan dengan membandingkan latensi dengan sistem lama. Hasil penelitian menunjukkan bahwa kerangka kerja ABizLSM telah berhasil memigrasikan sistem monolitik lama dalam perusahaan startup yang gesit, sehingga memberikan kemudahan pemeliharaan dan skalabilitas yang lebih baik serta meningkatkan kualitas sistem secara keseluruhan. Selain itu, tidak ada penurunan kinerja pada sistem yang telah dimigrasikan, sehingga kerangka kerja ABizLSM berhasil memigrasikan sistem monolitik lawas genting.</text>
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                <text>Bahasa  Minang  merupakan  bahasa  daerah  kelima  dengan  jumlah  penutur  terbanyak  di Indonesia, namun minim sumber daya linguistik dan teknologi pemrosesan bahasa alami yang mendukung.  Keterbatasan ini menyulitkan pengembangan aplikasi seperti mesin penerjemah dan pemeriksa ejaan otomatis.  Saat ini hanya tersedia korpus kesalahan ejaan dalam Bahasa Indonesia dengan kesalahan hanya satu karakter pada setiap token.  Korpus belum mencakup kesalahan penulisan kata serapan.  Selain itu belum ada korpus khusus yang dikembangkan untuk kesalahan ejaan dalam bahasa daerah di Indonesia, termasuk Bahasa Minang.  Penelitian ini bertujuan mengembangkan korpus kesalahan ejaan Bahasa Minang, yang dinamakan Spell Error Corpus for Minang Language (SPEML).  SPEML mencakup kesalahan ejaan sampai dengan tiga karakter dan kesalahan penulisan kata serapan.  Pengembangan SPEML melibatkan proses pengumpulan data korpus Bahasa Minang, data kata serapan yang sering digunakan, serta pembentukan korpus kesalahan ejaan.  Kesalahan ejaan dibentuk dengan mengacak token secara sistematis pada satu karakter, dua karakter, hingga tiga karakter, disesuaikan dengan panjang token. Hasil penelitian ini berupa SPEML yang mampu mengklasifikasikan tujuh jenis kesalahan  ejaan,  yaitu:  penyisipan  karakter,  penghapusan  karakter,  pindah  posisi  karakter, penggantian karakter, kesalahan tanda baca, kesalahan kata nyata, dan kesalahan penulisan kata serapan.  Pengembangan  SPEML  menjadi  langkah  awal  dalam  mendukung  pengembangan teknologi pemrosesan bahasa alami untuk bahasa daerah, khususnya Bahasa Minang.</text>
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                <text>Implementasi K-Means Clustering Melalui Pemanfaatan Sampling Kombinasi Pada Pengelompokan Pola Kesehatan Mental Mahasiswa Sains dan Teknologi</text>
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                <text>Kesehatan Mental,  K-Means Clustering,  Mahasiswa,  Sampling Kombinasi Stratifikasi,  Silhouette Coefficient</text>
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                <text>Kesehatan  mental  merupakan  aspek  kesehatan  penting  selain  kesehatan  fisik.  Mahasiswa merupakan individu yang berada pada usia remaja akhir sampai dewasa awal yang pada masa ini akan mengalami tekanan secara emosional karena masalah-masalah sosial, akademik, dan personal. Perlu diadakan pengecekan dini pada kesehatan mental mahasiswa seperti asesmen psikologi  yang  dilakukan  untuk  pencegahan  gangguan  mental  yang  dihadapi  mahasiswa sehingga  dapat  mengurangi  angka  bunuh  diri.  Tujuan  dari  penelitian  ini  adalah  untuk mendapatkan kelompok pola kesehatan mental mahasiswa untuk diidentifikasi pola dan tren dengan  algoritma  K-Means  clustering  dan  dievaluasi  dengan  silhouette  coefficient  untuk memastikan  keakuratan  dan  validitas  dari  hasil  clustering.  Data  penelitian  diperoleh  dari pengisian angket mengenai kondisi kesejahteraan psikologis (xଵ) dan tekanan psikologis (xଶ)yang  maing-masingnya  terdiri  dari  5  pertanyaan.  Penelitian  ini  memperoleh  hasil  setelah dikelompokkan menjadi 3 cluster yaitu tertekan (C1), netral/stabil (C2), dan bahagia (C3), pada mahasiswa sistem informasi tidak ada cluster yang dominan karena di setiap cluster memiliki jumlah  data  yang  sama,  mahasiswa  arsitektur  dan  matematika  dominan  mahasiswa  yang memiliki kesehatan mental yang tertekan, mahasiswa biologi dominan mahasiswanya memiliki kesehatan mental yang netral. Berdasarkan 4 program studi hasil evaluasi cluster pada program studi system informasi dan matematika memiliki struktur yang lemah, sedangkan pada program studi arsitektur dan biologi memiliki struktur yang sedang.</text>
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                <text>Firda Yunita Saria, Maharani Sukma Kuntarib,Winda Ari Yatic, Hani Khaulasarid,*, Moh. Hafiyusholehe</text>
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                <text>https://teknosi.fti.unand.ac.id/index.php/teknosi/issue/view/29</text>
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                <text>Universitas Islam Negeri Sunan Ampel Surabaya</text>
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                <text>30 april 2025</text>
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                <text>Fajar bagus W</text>
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                  <text>VOL 11 NO 1 (2025)</text>
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                <text>Empowering  MSMEs  with  Data-Driven  Insights:  Mobile  Sales  Dashboard Application for MSMEs</text>
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                <text>MSME,  Sales Dashboard,  Mobile Application, Data-Driven Decision Making </text>
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                <text>Micro, Small, and Medium Enterprises (MSMEs) in Indonesia have contributed significantly to GDP and the workforce, but still face challenges in managing sales data and making data-driven decisions.  Manual  recording  often  causes  operational  inefficiencies,  recording  errors,  and delays in business analysis. Based on the previous problem, this study develops a mobile-based sales dashboard application to help MSMEs analyse data in real-time and improve business strategies. The methodology used is Design Science Research (DSR) with a Rapid Application Development  (RAD)  approach  for  rapid  and  iterative  development.  This  application  was developed using Java and Firebase and provides sales summary features, best-selling cashiers, best-selling  products,  and  less  popular  products,  with  time  filters  and  graphical  data visualization. Testing using Black Box Testing shows that all features run well, while the User Acceptance Test (UAT) results show that 90.625% of users feel that this application is easy to use and suits their needs. These results indicate that the application can improve operational efficiency and business transparency and support data-driven decision-making for MSMEs.</text>
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                <text>Zalina Fatima Azzahra a,*, Riska Yanu Fa’rifah b, Syfa Nur Lathifah c</text>
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                <text>https://teknosi.fti.unand.ac.id/index.php/teknosi/issue/view/29</text>
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                <text>Telkom University</text>
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                <text>30 april 2025</text>
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                <text>Fajar Bagus W</text>
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