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All Journal Jurnal Media Infotama Sinergi Jurnal Komputer Terapan Jurnal TAM (Technology Acceptance Model) Jusikom : Jurnal Sistem Komputer Musirawas JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSIM (Jurnal Sistem Informasi Musirawas) Technologia: Jurnal Ilmiah Jurnal Sistem Komputer dan Informatika (JSON) Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Pengabdian kepada Masyarakat Nusantara Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Nasional Ilmu Komputer RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Pengembangan Sistem Informasi dan Informatika KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) BISMA Cendekia International Journal Cister Prosiding Seminar Riset Mahasiswa Jurnal Teknologi Informasi Mura PEDAMAS (Pengabdian Kepada Masyarakat) Adpebi Science Series Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal TAM (Technology Acceptance Model) Jurnal UNIV.BI MENGABDI
Lingga Wijaya, Harma Oktafia
Universitas Bina Insan, Lubuk Linggau, Indonesia
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PELATIHAN KOMPUTER UNTUK MENINGKAT SOFTSKILL DI DAFFA COMPUTER LUBUKLINGGAU Elmayati, Elmayati; Intan, Bunga; Wijaya, Harma Oktafia Lingga
JURNAL UNIV.BI MENGABDI Vol 3 No 1 (2024): Jurnal UNIV.BI Mengabdi : Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/mengabdi.v3i1.2319

Abstract

Pelaksanaan kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan keterampilan peserta dimana peserta terdiri dari siswa, mahasiswa, dan masyarakat umum. pelatihan computer ini dilaksanakan dengan cara mempraktekkan secara langsung lembar kerja Microsoft excel dengan mempraktekkan studi kasus yang ada di modul. Fokus dari pelatihan adalah pada peningkatan kemampuan peserta dalam mengoperasikan mocrosoft excel. Pelatihan diikuti oleh 20 peserta. Metode pelatihan meliputi survei, pelatihan, pendampingan dan evaluasi serta kebelanjutan program. Peserta diberi penjelasan mengenai lembar kerja Microsoft excel. Hasil yang diperoleh dari kegiatan ini yaitu peserta mempunyai keahlian dalam mengoperasikan computer khususnya Microsoft excel untuk meningkatkan keahlian dibidang computer. Secara keseluruhan, pelatihan ini memberikan dampak positif yang signifikan terhadap peserta, khususnya dibidang computer, serta diharapkan peserta lebih percaya diri dalam memasuki dunia kerja. Simpulan utama dari kegiatan pengabdian ini adalah kegiatan ini bisa memberikan manfaat yang besar bagi peserta yakni dengan memberikan keterampilan komputer, khususnya Microsoft excel.
KOLABORASI MAHASISWA DAN DOSEN ANTAR UNIVERSITAS UNTUK PEMBERDAYAAN UKM DAN HMP Intan, Bunga; Aktavera, Beni; Kurniawan, Rudi; Elmayati, Elmayati; Armanto, Armanto; Santoso, Budi; Daulay, Nelly Khairaini; Hidayat, Asep Toyib; Sobri, Ahmad; Wijaya, Harma Oktafia Lingga; Sari, Wisdalia Maya; Regina, Regina
JURNAL UNIV.BI MENGABDI Vol 4 No 1 (2025): Jurnal UNIV.BI Mengabdi : Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/mengabdi.v4i1.2696

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kapasitas Unit Kegiatan Mahasiswa (UKM) dan Himpunan Mahasiswa Program Studi (HMP) melalui kolaborasi dosen dan mahasiswa antar universitas. Kegiatan dilaksanakan di Universitas Dehasen Bengkulu pada tanggal 18–19 Juli 2025 dengan melibatkan 50 mahasiswa dari Program Studi Sistem Informasi, Informatika, dan Rekayasa Sistem Komputer. Metode yang digunakan adalah edukatif-partisipatif, yang meliputi pelatihan keorganisasian, praktik pembuatan website organisasi, pengenalan coding dasar, dan eksplorasi data science. Hasil kegiatan menunjukkan peningkatan signifikan pada pemahaman peserta, yang ditunjukkan oleh kenaikan rata-rata nilai post-test dari 55 menjadi 83. Peserta juga menghasilkan produk nyata berupa website organisasi dan skrip coding sederhana. Dampak dari kegiatan ini adalah peningkatan kapasitas manajerial dan literasi digital mahasiswa, terbentuknya jejaring kolaborasi antar perguruan tinggi, serta mendorong UKM dan HMP mitra untuk mengoptimalkan peran mereka secara profesional dan ino
PEMBERDAYAAN GURU SMK MUHAMMADIYAH LUBUK LINGGAU MELALUI PELATIHAN PEMANFAATAN ARTIFICIAL INTELLEGENCE UNTUK MENDUKUNG PEMBELAJARAN DIGITAL Wijaya, Harma Oktafia Lingga; Intan, Bunga; Armanto, Armanto
JURNAL UNIV.BI MENGABDI Vol 4 No 2 (2025): Jurnal UNIV.BI Mengabdi : Desember
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/mengabdi.v4i2.2921

Abstract

Perkembangan teknologi informasi dan komunikasi mendorong terjadinya transformasi pembelajaran menuju pembelajaran digital yang kreatif, adaptif, dan berbasis teknologi. Salah satu tantangan utama dalam transformasi tersebut adalah kesiapan dan kompetensi guru dalam memanfaatkan teknologi lanjutan, khususnya koding dan kecerdasan artifisial (Artificial Intelligence/AI), sebagai bagian dari pembelajaran abad ke-21. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk memberdayakan guru SMK Muhammadiyah Lubuklinggau melalui pelatihan pemanfaatan AI guna mendukung pembelajaran digital yang inovatif dan kontekstual. Kegiatan PKM dilaksanakan selama dua hari, yaitu pada tanggal 16–17 Juli 2025, oleh tim yang terdiri dari tujuh orang dosen dan mahasiswa Fakultas Komputer Universitas Bina Insan yang berkolaborasi dengan Himpunan Mahasiswa Program Studi Sistem Informasi. Metode pelaksanaan meliputi sosialisasi, penyampaian materi, praktik langsung, penugasan berbasis proyek, presentasi hasil, serta refleksi dan evaluasi kegiatan. Setiap peserta diwajibkan menghasilkan proyek pembelajaran berbasis AI sebagai bentuk implementasi dan evaluasi pemahaman materi. Hasil kegiatan menunjukkan bahwa pelatihan ini mampu meningkatkan tingkat pemahaman guru terhadap pemanfaatan AI dalam pembelajaran digital hingga mencapai 90%. Guru menunjukkan antusiasme tinggi serta kemampuan awal dalam merancang pembelajaran berbasis teknologi yang lebih kreatif dan relevan dengan kebutuhan pendidikan vokasi. Kegiatan ini membuktikan bahwa pelatihan yang terstruktur dan aplikatif dapat meningkatkan kompetensi digital guru serta mendukung implementasi kebijakan pembelajaran koding dan kecerdasan artifisial di satuan pendidikan
Sistem Prediksi Pertumbuhan Ekonomi Kabupaten Musi Rawas, Kabupaten Musi Rawas Utara Dan Kota Lubuklinggau Dengan Metode Regresi Linier Andri Anto Tri S; Armanto Armanto; Harma Oktafia Lingga Wijaya; Wisdalia Maya Sari
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 4 (2022): Juni 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4198

Abstract

The economic condition of a region in each period can increase or decrease by looking at changes in goods and services. An increase in economic activity is a process of changing economic conditions that occur in an area on an ongoing basis to get to a better state for a certain period of time. Economic growth is a benchmark in achieving the development of economic conditions in a region so that it has an impact on increasing people's welfare. South Sumatra's economic growth in the first quarter of 2021 improved compared to the previous quarter. Similar to economic growth in South Sumatra Province, the districts and cities in it (Musi Rawas Regency, North Musi Rawas and Lubuklinggau City) also experienced ups and downs of economic growth. With the current ups and downs of economic growth, Musi Rawas Regency, North Musi Rawas and Lubuklinggau City need accurate information about the picture of economic growth in the future, this is intended to be able to prepare various policies or actions so that the level of the economy in Musi Rawas Regency, Musi North Rawas and Lubuklinggau City can be increased. Based on this problem, Musi Rawas Regency, North Musi Rawas and Lubuklinggau City need a prediction system in order to see a picture of economic growth in the future. The purpose of this study is to design a prediction system that can predict the rate of economic growth in Musi Rawas Regency, North Musi Rawas and Lubuklinggau City. The method used in the prediction system is a simple linear regression method, the use of a simple linear regression method in this study due to the limited time of the study and used to determine the direction of the relationship between the independent variable and the dependent variable, whether it has a positive or negative relationship and to predict the value of the dependent variable if the value of the independent variable increases or decreases.
Prediksi Pola Penjualan Barang pada UMKM XYZ dengan Metode Algoritma Apriori Harma Oktafia Lingga Wijaya; Andri Anto Tri. S; A Armanto; Wisdalia Maya Sari
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 4 (2022): Juni 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4200

Abstract

Through the development of information technology today, the need for clear and accurate information is needed in everyday life, so that information will become an important thing in society. Sometimes high information needs are not accompanied by the presentation of adequate information, often information through the mining process is expected to provide information that was previously hidden in the data warehouse so that it becomes important and valuable information [1]. Utilization of existing data in the information system to support decision-making activities, it is not enough to just rely on operational data, a data analysis is needed to explore the potential of existing information. Decision makers try to take advantage of existing data warehouses to explore useful information to help make decisions, this encourages the emergence of new branches of science to overcome the problem of extracting important or interesting information or patterns from large amounts of data, which is called data mining. 2]. MSME XYZ is one of the leading MSMEs in Lubuklinggau City where this MSME sells various kinds of durian products such as tempoyak, lempok durian, peeled durian, durian pancakes, durian ice cream, durian coffee, durian seed chips etc. Every day MSME XYZ carries out activities such as sales transactions, providing product stock and so on, from the existing sales data so far XYZ has not been able to provide information about the pattern of customer spending habits so that transaction data cannot help leaders in making decisions from data collected. there is. Association analysis or association rule mining is a data mining technique to find the rules of a combination of items. One of the stages of association analysis that has attracted the attention of many researchers to produce efficient algorithms is high frequency pattern analysis (frequent pattern mining). The output of data mining can be used to improve decision making in the future.
INTEGRASI APRIORI & FP-GROWTH PADA BUSINESS INTELLIGENCE SYSTEM UNTUK OPTIMALISASI STRATEGI PENJUALAN Lestari, Mita; Wijaya, Harma Oktafia Lingga
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10910

Abstract

Grocery stores as small-scale retail businesses generate large volumes of sales transaction data that have not been optimally utilized to support business decision-making. These transaction data contain valuable information in the form of customer purchasing patterns that can be analyzed using association rule mining techniques. This study aims to integrate the Apriori and FP-Growth methods into a Business Intelligence System to optimize sales strategies in a grocery store.Sales transaction data were processed using the WEKA application by applying two association rule mining methods, namely Apriori and FP-Growth. The Apriori method was implemented with a minimum confidence value of 90%, while the FP-Growth method used a minimum confidence value of 55%. The results show that the Apriori method produces association rules with a higher level of confidence, particularly for combinations of staple products such as rice, cooking oil, sugar, and instant noodles. Meanwhile, the FP-Growth method generates a wider variety of association rules with lower confidence values but offers superior computational efficiency in terms of processing time.A comparative analysis indicates that the Apriori method is more effective in producing highly reliable association rules for specific strategic recommendations, whereas the FP-Growth method is more suitable for exploring overall purchasing patterns with lower computational complexity. The integration of both methods into a Business Intelligence System provides strategic recommendations related to inventory management, product placement, and promotion planning based on customer purchasing behavior. Therefore, the proposed integration of Apriori and FP-Growth is expected to enhance sales strategy effectiveness and improve the competitiveness of grocery stores.
ANALISIS POLA PEMILIHAN JALUR PENDAKIAN PENGUNJUNG DALAM MENDUKUNG BUSINESS INTELLIGENCE PARIWISATA Pitri, Mawar; Wijaya, Harma Oktafia Lingga
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10953

Abstract

This study aims to analyze visitor behavior patterns in selecting hiking trails at Mount as a basis for decision-making driven by Business Intelligence (BI). The primary issue addressed is the congestion of hikers on specific trails, which risks ecological degradation and diminishes the quality of the tourism experience. This research employs two main methods: Market Basket Analysis (Association Rules) to map hikers' equipment needs based on their chosen trails, and Spatial Zoning Analysis to manage visitor density distribution in real-time.The results indicate a strong association between the "Kawah Hidup" trail selection and the demand for masks, as well as night hiking trails with the rental of flashlights and heavy jackets. Based on these patterns, a service segmentation strategy was developed through four primary packages: "The Adventurer" (Jungle Trail), "The Express Sightseer" (Asphalt Trail), "Summit Explorer" (Gajah Peak & Dead Crater), and "Kawah Hidup Exclusive". Business Intelligence is implemented through digital information boards for zoning management and loyalty programs for local hikers to maintain the sustainability of jungle trails. The conclusion of this research confirms that optimizing logistics stock and digitalizing trail information enhances operational efficiency and supports the ecological sustainability of the Mount forest area.
PREDIKSI HARGA SAHAM BANK MANDIRI BERDASARKAN DATA BMRI HISTORICAL STOCK PRICE Mahmud, Khairul Imam; Kurniawan, Rudi; Wijaya, Harma Oktafia Lingga
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10983

Abstract

Stock price movements exhibit dynamic and highly fluctuating characteristics, making accurate prediction challenging when using conventional approaches. Therefore, this study aims to develop and evaluate a stock price prediction model for PT Bank Mandiri (Persero) Tbk using a deep learning approach based on Long Short-Term Memory (LSTM). The LSTM model is applied for time series forecasting by utilizing historical stock price data, with a primary focus on the closing price as the target variable. The historical stock price data of Bank Mandiri undergo preprocessing stages, including data cleaning and normalization using the Min–Max Scaling method, to align data scales and improve training stability and convergence. The proposed LSTM architecture consists of two LSTM layers with dropout mechanisms for regularization, followed by fully connected layers to generate stock price predictions. The model is trained using the Adam optimizer with Mean Squared Error (MSE) as the loss function. Model performance is evaluated using the Mean Absolute Percentage Error (MAPE) metric on the testing dataset. The experimental results show that the LSTM model achieves a MAPE value of 2.7572%, indicating a very high prediction accuracy. Furthermore, the future forecasting results suggest a relatively stable stock price movement with a gradual upward trend in the short term. Based on the findings, it can be concluded that the Long Short-Term Memory (LSTM) method is effective for predicting Bank Mandiri’s stock prices and has strong potential as a data-driven decision support tool for investment analysis, although it should be complemented with fundamental analysis and external market factors
Co-Authors A.A. Ketut Agung Cahyawan W Adi Saputra Aditya Nugroho Aktavera, Beni Ali Amran Andri Anto Tri Susilo, Andri Anto Anita Sari Aprisusanti, Rani Purnama Armanto Armanto Armanto Armanto Armanto armanto armanto Armanto, A Armanto, Armanto Audya, Rosa Ayu Fitria, Sekar Basmansyah, Attaya Rifki Beni Aktavera Beni Mandala Putra Biankha Ariesty Bunga Intan Chandra Yuliansyah Cindi Wulandari Daulay, Nelly Khairaini Daulay, Nelly Khairani Davit Irawan Devi Purwanti, Devi Dwi Budi Santoso E Elmayati Elmayati Elmayati, E Elmayati, Elmayati Fachry Abda El Rahman Fauzan, Muazim Rahman Fienda Altamevia Handayani , Diah Fitria Hartansyah, Dendi Hidayat, Asep Toyib Intan, Bunga Jeki Saputra joni karman Karman, Joni Khoirina Khoirina Lestari, Mita Mahmud, Khairul Imam Marlina, Haiza Mediriansyah Mediriansyah Mohamad, Fatma Susilawati nansyah, Satria Nopalia Nopalia Nur Alamsyah, Muhammad Nurdiansyah, Deni Pitri, Mawar Rani Purnama Aprisusanti Regina Regina Rudi Kurniawan Rusdiyanto Rusdiyanto Rusdiyanto Rusdiyanto, Rusdiyanto Samsuryadi Samsuryadi Sari, Wisdalia Maya Satrianansyah, Satrianansyah Selviyani sobri, ahmad Soleha, Romi Aprillia Sujayanti, Elies Fifit Susanto Susanto Susanto Susanto Susanto Syamsul Arifin, M. Agus Taufik Rahman Tri Hasanah Bimastari Aviani Wisdalia Maya Sari Wisdalia Maya Sari Wulandari, Cindi Yunita Yunita Yunita Yunita Zulhipni Reno Saputra Els Zulius, Antoni